24.8.4 ATR -- Vehicles, Aerial Images, Vehicle Detection

Klausmann, P., Kroschel, K., Willersinn, D.,
Performance prediction of vehicle detection algorithms,
PR(32), No. 12, December 1999, pp. 2063-2065.
Elsevier DOI BibRef 9912

Tang, G.Y., Huang, T.S.,
Using the Creation Machine to Locate Airplanes on Aerial Photos,
PR(12), No. 6, 1980, pp. 431-441.
Elsevier DOI BibRef 8000

Solka, J.L., Marchette, D.J., Wallet, B.C., Irwin, V.L., and Rogers, G.W.,
Identification of Man-Made Regions in Unmanned Aerial Vehicle Imagery and Videos,
PAMI(20), No. 8, August 1998, pp. 852-857.
IEEE DOI Statistical classification to find regions of interest in such data. BibRef 9808

Trivedi, M.M.[Mohan M.], and Harlow, C.A.[Charles A.],
Identification of Unique Objects in High Resolution Aerial Images,
OptEng(24), No. 3, May-June 1985, pp. 502-506. BibRef 8505

Trivedi, M.M., and Harlow, C.A.,
Incorporating Spectral and Spatial Knowledge in the Identification of Unique Objects in High Resolution Aerial Images,
SPIE(485), May 1984, pp. 214-219. BibRef 8405

Trivedi, M.M., Bokil, A., Takla, M., Maksymonko, G.B., and Broach, T.,
Analysis of High Resolution Imagery for Object Detection,
SPIE(1099), March 1989. BibRef 8903

Trivedi, M.M.,
Object Detection Using Their Multispectral Characteristics,
SPIE(754), January 1987. pp. xx. BibRef 8701

Shirvaikar, M.V., and Trivedi, M.M.,
Design and Evaluation of a Multistage Object Detection Approach,
SPIE(1293), Applications of AI VIII, Orlando, FL, April 1990, pp. 14-22. BibRef 9004

Trivedi, M.M.[Mohan M.],
Object Detection in High Resolution Multispectral Images,
SPIE(933), Multispectral Image Processing and Enhancement, Orlando, FL, April 1988, pp. 8-14. Pyramids. BibRef 8804

Trivedi, M.M., Harlow, C.A., Cress, D.H., and Chen, C.,
Detection of Objects in High Resolution Multispectral Aerial Images,
SPIE(548), Applications of Artificial Intelligence II, Arlington, VA, April 1985, pp. 258-262. BibRef 8504

Moon, H.K.[Han-Kyu], Chellappa, R., Rosenfeld, A.,
Performance Analysis of a Simple Vehicle Detction Algorithm,
IVC(20), No. 1, January 2002, pp. 1-13.
Elsevier DOI 0201
BibRef
Earlier: UMD--TR4045, August 1999. Empirical Evaluation. Vehicle Counting.
WWW Link. BibRef

Burlina, P., Parameswaran, V., Chellappa, R.,
Sensitivity Analysis and Learning Strategies for Context-Based Vehicle Detection Algorithms,
DARPA97(577-584). BibRef 9700

Rajagopalan, A.N.[Ambasamudram N.], Chellappa, R.[Rama],
Higher-Order-Statistics-Based Detection of Vehicles in Still Images,
JOSA-A(18), No. 12, December 2001, pp. 3037-3048.
WWW Link. 0201
BibRef
Earlier: UMD--TR4189, September 2000.
WWW Link. BibRef

Rajagopalan, A.N., Burlina, P., Chellappa, R.,
Higher Order Statistical Learning for Vehicle Detection in Images,
ICCV99(1204-1209).
IEEE DOI BibRef 9900

Zhao, T.[Tao], Nevatia, R.[Ram],
Car Detection in Low Resolution Aerial Images,
IVC(21), No. 8, August 2003, pp. 693-703.
Elsevier DOI 0307
BibRef
Earlier: ICCV01(I: 710-717).
IEEE DOI 0106
BibRef USC Computer VisionUse a simple generic 3-D model of the car. Match edge features. BibRef

Howard, D.[Daniel], Roberts, S.C.[Simon C.], Ryan, C.[Conor],
Pragmatic Genetic Programming strategy for the problem of vehicle detection in airborne reconnaissance,
PRL(27), No. 11, August 2006, pp. 1275-1288.
Elsevier DOI Genetic Programming; Method of stages; Discrete Fourier transform 0606
BibRef

Pegler, K.[Kevin], Colemen, D.[David], Pelot, R.[Ronald], Keller, C.P.[C. Peter],
An Enhanced Spatio-spectral Template for Automatic Small Recreational Vessel Detection,
PhEngRS(73), No. 1, January 2007, pp. 79-88.
WWW Link. 0704
The utility of an enhanced spatio-spectral template used in conjunction with Ikonos imagery to detect small recreational vessels. BibRef

Jin, X.Y.[Xiao-Ying], Davis, C.H.[Curt H.],
Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks,
IVC(25), No. 9, 1 September 2007, pp. 1422-1431.
Elsevier DOI 0707
Vehicle detection; High-resolution satellite imagery; Neural networks; Feature extraction; IKONOS BibRef

Xiong, Z.[Zhen], Zhang, Y.[Yun],
An Initial Study on Vehicle Information Extraction from Single Pass QuickBird Satellite Imagery,
PhEngRS(74), No. 11, November 2008, pp. 1401-1412.
WWW Link. 0804
A direct location algorithm to calculate vehicle's ground position from its image position based on a Digital Elevation Model.
See also Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images, A. BibRef

Eikvil, L.[Line], Aurdal, L.[Lars], Koren, H.[Hans],
Classification-based vehicle detection in high-resolution satellite images,
PandRS(64), No. 1, January 2009, pp. 65-72.
Elsevier DOI 0804
Quickbird; Vehicle detection; Classification BibRef

Yue, Z.F.[Zhan-Feng], Guarino, D.[David], Chellappa, R.[Rama],
Moving Object Verification in Airborne Video Sequences,
CirSysVideo(19), No. 1, January 2009, pp. 77-89.
IEEE DOI 0902
BibRef
Earlier: A1, A3, A2:
Moving Object Verification from Airborne Video,
CVS06(29).
IEEE DOI 0602
BibRef

Holt, A.C.[Ashley C.], Seto, E.Y.W.[Edmund Y.W.], Rivard, T.[Tom], Gong, P.[Peng],
Object-based Detection and Classification of Vehicles from High-resolution Aerial Photography,
PhEngRS(75), No. 7, July 2009, pp. 871-897.
WWW Link. 0910
An object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout a large metropolitan area. BibRef

Chang, W.C., Cho, C.W.,
Online Boosting for Vehicle Detection,
SMC-B(40), No. 3, June 2010, pp. 892-902.
IEEE DOI 1006
BibRef

Cao, X.B.[Xian-Bin], Wu, C., Lan, J.H.[Jin-He], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment,
CirSysVideo(21), No. 10, October 2011, pp. 1522-1533.
IEEE DOI 1110
BibRef
Earlier: A1, A3, A4, A5:
KLT Feature Based Vehicle Detection and Tracking in Airborne Videos,
ICIG11(673-678).
IEEE DOI 1109
BibRef

Cao, X.B.[Xian-Bin], Lin, R.J.[Ren-Jun], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment,
CirSysVideo(22), No. 3, March 2012, pp. 366-378.
IEEE DOI 1203
BibRef
Earlier:
Accelerating Vehicle Detection in Low-Altitude Airborne Urban Video,
ICIG11(648-653).
IEEE DOI 1109
BibRef

Xia, Y.J.[Ying-Jie], Zhang, L.M.[Lu-Ming], Liu, Z.G.[Zhen-Guang], Nie, L.Q.[Li-Qiang], Li, X.L.[Xue-Long],
Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs,
IP(26), No. 8, August 2017, pp. 3748-3758.
IEEE DOI 1707
binary codes, colour photography, cryptography, feature extraction, image matching, support vector machines, aerial photograph categorization, binary codes, graphlet number extraction, graphlet-to-graphlet matching, hash-based graphlet, high-level semantic cue encoding, image kernel, local image patches, multiclass SVM, BibRef

Bai, X.[Xueru], Zhou, F.[Feng], Xing, M.D.[Meng-Dao], Bao, Z.[Zheng],
A Novel Method for Imaging of Group Targets Moving in a Formation,
GeoRS(50), No. 1, January 2012, pp. 221-231.
IEEE DOI 1201

See also Sparse Subband Imaging of Space Targets in High-Speed Motion. BibRef

Bai, X., Xing, M., Zhou, F., Lu, G., Bao, Z.,
Imaging of Micromotion Targets With Rotating Parts Based on Empirical-Mode Decomposition,
GeoRS(46), No. 11, November 2008, pp. 3514-3523.
IEEE DOI 0812
BibRef

Cheng, H.Y., Weng, C.C., Chen, Y.Y.,
Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks,
IP(21), No. 4, April 2012, pp. 2152-2159.
IEEE DOI 1204
BibRef

Frolind, P.O., Gustavsson, A., Lundberg, M., Ulander, L.M.H.,
Circular-Aperture VHF-Band Synthetic Aperture Radar for Detection of Vehicles in Forest Concealment,
GeoRS(50), No. 4, April 2012, pp. 1329-1339.
IEEE DOI 1204
BibRef

Cao, X.B.[Xian-Bin], Lan, J.H.[Jin-He], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Vehicle detection and tracking in airborne videos by multi-motion layer analysis,
MVA(23), No. 5, September 2012, pp. 921-935.
WWW Link. 1208
BibRef

Cao, X.B.[Xian-Bin], Wu, C.X.[Chang-Xia], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos,
ICIP11(2421-2424).
IEEE DOI 1201
BibRef

Schindler, K.,
An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification,
GeoRS(50), No. 11, November 2012, pp. 4534-4545.
IEEE DOI 1210
BibRef

Tokarczyk, P., Montoya, J., Schindler, K.,
An Evaluation of Feature Learning Methods for High Resolution Image Classification,
AnnalsPRS(I-3), No. 2012, pp. 389-394.
DOI Link 1209
Lots of small (urban) objects. BibRef

Butler, P.[Patrick], Ramakrishnan, N.[Naren], Nsoesie, E.O.[Elaine O.], Brownstein, J.S.[John S.],
Satellite Imagery Analysis: What Can Hospital Parking Lots Tell Us about a Disease Outbreak?,
Computer(47), No. 4, April 2014, pp. 94-97.
IEEE DOI 1405
Data mining BibRef

Jabbar, S.[Sana], Akbar, A.[Ali], Zafar, S.[Saima], Quddoos, M.[Muhammad], Hussain, M.[Majid],
VISTA: achieving cumulative VIsion through energy efficient Silhouette recognition of mobile Targets through collAboration of visual sensor nodes,
JIVP(2014), No. 1, 2014, pp. 32.
DOI Link 1407
BibRef

Moranduzzo, T., Melgani, F.,
Detecting Cars in UAV Images With a Catalog-Based Approach,
GeoRS(52), No. 10, October 2014, pp. 6356-6367.
IEEE DOI 1407
Catalogs BibRef

Bazi, Y., Melgani, F.,
Convolutional SVM Networks for Object Detection in UAV Imagery,
GeoRS(56), No. 6, June 2018, pp. 3107-3118.
IEEE DOI 1806
Feature extraction, Object detection, Remote sensing, Spatial resolution, Standards, Support vector machines, Training, unmanned aerial vehicle (UAV) platforms BibRef

Mekhalfi, M.L.[Mohamed Lamine], Bejiga, M.B.[Mesay Belete], Soresina, D.[Davide], Melgani, F.[Farid], Demir, B.[Begüm],
Capsule Networks for Object Detection in UAV Imagery,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Gao, F.[Feng], Li, B.[Bo], Xu, Q.Z.[Qi-Zhi], Zhong, C.[Chen],
Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis,
RS(6), No. 7, 2014, pp. 6500-6523.
DOI Link 1408
BibRef

Zhang, J.X.[Ji-Xian], Duan, M.[Minyan], Yan, Q.[Qin], Lin, X.G.[Xiang-Guo],
Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method,
RS(6), No. 9, 2014, pp. 8405-8423.
DOI Link 1410
BibRef

Zhang, X.P.[Xue-Pan], Liao, G.S.[Gui-Sheng], Zhu, S.Q.[Sheng-Qi], Zeng, C.[Cao], Shu, Y.X.[Yu-Xiang],
Geometry-Information-Aided Efficient Radial Velocity Estimation for Moving Target Imaging and Location Based on Radon Transform,
GeoRS(53), No. 2, February 2015, pp. 1105-1117.
IEEE DOI 1411
Radon transforms BibRef

Börcs, A.[Attila], Benedek, C.[Csaba],
Extraction of Vehicle Groups in Airborne Lidar Point Clouds With Two-Level Point Processes,
GeoRS(53), No. 3, March 2015, pp. 1475-1489.
IEEE DOI 1412
BibRef
Earlier:
A Marked Point Process Model For Vehicle Detection In Aerial Lidar Point Clouds,
AnnalsPRS(I-3), No. 2012, pp. 93-98.
DOI Link 1209
feature extraction
See also Detection of soldering defects in Printed Circuit Boards with Hierarchical Marked Point Processes. BibRef

Taoufiq, S.[Salma], Nagy, B.[Balázs], Benedek, C.[Csaba],
HierarchyNet: Hierarchical CNN-Based Urban Building Classification,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zováthi, Ö.[Örkény], Nagy, B.[Balázs], Benedek, C.[Csaba],
Exploitation of Dense MLS City Maps for 3d Object Detection,
ICIAR20(I:393-403).
Springer DOI 2007
BibRef

Börcs, A.[Attila], Nagy, B.[Balázs], Baticz, M.[Milán], Benedek, C.[Csaba],
A Model-Based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds,
AutoSystems14(413-425).
Springer DOI 1504
BibRef

Börcs, A.[Attila], Nagy, B.[Balázs], Benedek, C.[Csaba],
Fast 3-D Urban Object Detection on Streaming Point Clouds,
CVRoads14(628-639).
Springer DOI 1504
BibRef

Janney, P.[Pranam], Booth, D.[David],
Pose-invariant vehicle identification in aerial electro-optical imagery,
MVA(26), No. 5, July 2015, pp. 575-591.
WWW Link. 1506
BibRef

Dickson, C.N.[Christopher N.], Wallace, A.M.[Andrew M.], Kitchin, M.[Matthew], Connor, B.[Barry],
Long-wave infrared polarimetric cluster-based vehicle detection,
JOSA-A(32), No. 12, December 2015, pp. 2307-2315.
DOI Link 1601
Pattern recognition; Polarimetric imaging BibRef

Razakarivony, S.[Sebastien], Jurie, F.[Frederic],
Vehicle detection in aerial imagery: A small target detection benchmark,
JVCIR(34), No. 1, 2016, pp. 187-203.
Elsevier DOI 1601
Detection BibRef

Chen, Z.Y.[Zi-Yi], Wang, C.[Cheng], Wen, C.L.[Cheng-Lu], Teng, X.H.[Xiu-Hua], Chen, Y.P.[Yi-Ping], Guan, H.Y.[Hai-Yan], Luo, H.[Huan], Cao, L.J.[Liu-Juan], Li, J.,
Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels,
GeoRS(54), No. 1, January 2016, pp. 103-116.
IEEE DOI 1601
feature extraction BibRef

Chen, Z.Y.[Zi-Yi], Wang, C.[Cheng], Luo, H.[Huan], Wang, H., Chen, Y.P.[Yi-Ping], Wen, C.L.[Cheng-Lu], Yu, Y., Cao, L.J.[Liu-Juan], Li, J.,
Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature,
ITS(17), No. 8, August 2016, pp. 2296-2309.
IEEE DOI 1608
Dictionaries BibRef

Yu, Y.T.[Yong-Tao], Guan, H.Y.[Hai-Yan], Zai, D.W.[Da-Wei], Ji, Z.[Zheng],
Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests,
PandRS(112), No. 1, 2016, pp. 50-64.
Elsevier DOI 1602
Airplane detection BibRef

Cao, Y.T.[Yu-Tian], Wang, G.[Gang], Yan, D.M.[Dong-Mei], Zhao, Z.M.[Zhong-Ming],
Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences,
RS(8), No. 1, 2016, pp. 28.
DOI Link 1602
BibRef

Razakarivony, S.[Sebastien], Jurie, F.[Frederic],
A novel target detection algorithm combining foreground and background manifold-based models,
MVA(27), No. 3, April 2016, pp. 363-375.
Springer DOI 1604
Vehicles in aerial images. BibRef

Cao, L.J.[Liu-Juan], Luo, F.[Feng], Chen, L.[Li], Sheng, Y.[Yihan], Wang, H.B.[Hai-Bin], Wang, C.[Cheng], Ji, R.R.[Rong-Rong],
Weakly supervised vehicle detection in satellite images via multi-instance discriminative learning,
PR(64), No. 1, 2017, pp. 417-424.
Elsevier DOI 1701
Multiple instance learning BibRef

Sheng, Y., Cao, L., Wang, C., Li, J.,
Weakly Supervised Vehicle Detection in Satellite Images via Multiple Instance Ranking,
ICPR18(2765-2770)
IEEE DOI 1812
Proposals, Vehicle detection, Satellites, Feature extraction, Remote sensing, Detectors, Task analysis BibRef

Ammour, N.[Nassim], Al Hichri, H.[Haikel], Bazi, Y.[Yakoub], Benjdira, B.[Bilel], Alajlan, N.[Naif], Zuair, M.[Mansour],
Deep Learning Approach for Car Detection in UAV Imagery,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Al Rahhal, M.M.[Mohamad M.], Bazi, Y.[Yakoub], Abdullah, T.[Taghreed], Mekhalfi, M.L.[Mohamed L.], Al Hichri, H.[Haikel], Zuair, M.[Mansour],
Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Audebert, N.[Nicolas], Le Saux, B.[Bertrand], Lefèvre, S.[Sébastien],
Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Li, F.[Feimo], Li, S.X.[Shu-Xiao], Zhu, C.F.[Cheng-Fei], Lan, X.S.[Xiao-Song], Chang, H.X.[Hong-Xing],
Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef
And:
Class-imbalance aware CNN extension for high resolution aerial image based vehicle localization and categorization,
ICIVC17(761-765)
IEEE DOI 1708
class-imbalance, convolutional neural network, high resolution aerial image, vehicle categorization, vehicle, detection BibRef

El Mikaty, M., Stathaki, T.,
Detection of Cars in High-Resolution Aerial Images of Complex Urban Environments,
GeoRS(55), No. 10, October 2017, pp. 5913-5924.
IEEE DOI 1710
object detection, image descriptors, urban environments, Roads, automatic target recognition BibRef

Koga, Y.[Yohei], Miyazaki, H.[Hiroyuki], Shibasaki, R.[Ryosuke],
A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Schilling, H.[Hendrik], Bulatov, D.[Dimitri], Middelmann, W.[Wolfgang],
Object-based detection of vehicles using combined optical and elevation data,
PandRS(136), 2018, pp. 85-105.
Elsevier DOI 1802
Vehicle detection, Object-based classification, Data fusion, Elevation data, Random forest, High-resolution, Cluster analysis BibRef

Chen, Z.[Zhong], Zhang, T.[Ting], Ouyang, C.[Chao],
End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Bashmal, L.[Laila], Bazi, Y.[Yakoub], Al Hichri, H.[Haikel], Al Rahhal, M.M.[Mohamad M.], Ammour, N.[Nassim], Alajlan, N.[Naif],
Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
generative adversarial networks (GANs). BibRef

Yang, C.X.[Chen-Xi], Li, W.J.[Wen-Jing], Lin, Z.Y.[Zhi-Yong],
Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Mou, L., Zhu, X.X.,
Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network,
GeoRS(56), No. 11, November 2018, pp. 6699-6711.
IEEE DOI 1811
Image segmentation, Semantics, Feature extraction, Vehicle detection, Remote sensing, Task analysis, Object detection, vehicle detection BibRef

Solmaz, B.[Berkan], Gundogdu, E.[Erhan], Yucesoy, V.[Veysel], Koç, A.[Aykut], Alatan, A.A.[Abdullah Aydin],
Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding,
IET-CV(12), No. 8, December 2018, pp. 1121-1132.
DOI Link 1812
BibRef

Chaari, L.[Lotfi],
A Bayesian grouplet transform,
SIViP(13), No. 5, July 2019, pp. 871-878.
WWW Link. 1906
BibRef

Gharbi, W.[Walma], Chaari, L.[Lotfi], Benazza-Benyahia, A.[Amel],
Bayesian Vehicle Detection Using Optical Remote Sensing Images,
ACIVS18(258-269).
Springer DOI 1810
BibRef

Cao, S.[Shuang], Yu, Y.T.[Yong-Tao], Guan, H.Y.[Hai-Yan], Peng, D.F.[Dai-Feng], Yan, W.Q.[Wan-Qian],
Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Sommer, L.W.[Lars Wilko], Schuchert, T.[Tobias], Beyerer, J.[Jürgen],
Comprehensive Analysis of Deep Learning-Based Vehicle Detection in Aerial Images,
CirSysVideo(29), No. 9, September 2019, pp. 2733-2747.
IEEE DOI 1909
BibRef
Earlier:
Fast Deep Vehicle Detection in Aerial Images,
WACV17(311-319)
IEEE DOI 1609
BibRef
Earlier:
A comprehensive study on object proposals methods for vehicle detection in aerial images,
PRRS16(1-6)
IEEE DOI 1704
Vehicle detection, Machine learning, Image edge detection, Detectors, Image segmentation, deep learning. Computational efficiency, Detectors, Image edge detection, Image segmentation, Proposals, image classification BibRef

Sommer, L.W.[Lars Wilko], Schumann, A., Schuchert, T.[Tobias], Beyerer, J.[Jürgen],
Multi Feature Deconvolutional Faster R-CNN for Precise Vehicle Detection in Aerial Imagery,
WACV18(635-642)
IEEE DOI 1806
deconvolution, image classification, learning (artificial intelligence), neural nets, Training BibRef

Zheng, K.[Kun], Wei, M.F.[Meng-Fei], Sun, G.M.[Guang-Min], Anas, B.[Bilal], Li, Y.[Yu],
Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Tao, C., Mi, L., Li, Y., Qi, J., Xiao, Y., Zhang, J.,
Scene Context-Driven Vehicle Detection in High-Resolution Aerial Images,
GeoRS(57), No. 10, October 2019, pp. 7339-7351.
IEEE DOI 1910
feature extraction, geophysical image processing, image classification, image resolution, image segmentation, vehicle detection BibRef

Zhang, J.P.[Jun-Peng], Jia, X.P.[Xiu-Ping], Hu, J.K.[Jian-Kun],
Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Feng, J.[Jie], Zeng, D.[Dening], Jia, X.P.[Xiu-Ping], Zhang, X.R.[Xiang-Rong], Li, J.[Jie], Liang, Y.P.[Yu-Ping], Jiao, L.C.[Li-Cheng],
Cross-frame keypoint-based and spatial motion information-guided networks for moving vehicle detection and tracking in satellite videos,
PandRS(177), 2021, pp. 116-130.
Elsevier DOI 2106
Deep learning, Keypoint-based detection, Moving vehicle detection, Multi-object tracking, Satellite videos BibRef

Shao, J., Du, B., Wu, C., Zhang, L.,
Tracking Objects From Satellite Videos: A Velocity Feature Based Correlation Filter,
GeoRS(57), No. 10, October 2019, pp. 7860-7871.
IEEE DOI 1910
feature extraction, image colour analysis, image filtering, image representation, image sequences, object detection, velocity feature BibRef

Yang, Z., Liu, Y., Liu, L., Tang, X., Xie, J., Gao, X.,
Detecting Small Objects in Urban Settings Using SlimNet Model,
GeoRS(57), No. 11, November 2019, pp. 8445-8457.
IEEE DOI 1911
Feature extraction, Licenses, Urban areas, Deep learning, Object detection, Roads, urban element detection (UED) BibRef

Zhang, X.[Xunxun], Zhu, X.[Xu],
An Efficient and Scene-Adaptive Algorithm for Vehicle Detection in Aerial Images Using an Improved YOLOv3 Framework,
IJGI(8), No. 11, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Xie, W.Y.[Wei-Ying], Yang, J.[Jian], Li, Y.S.[Yun-Song], Lei, J.[Jie], Zhong, J.P.[Jia-Ping], Li, J.J.[Jiao-Jiao],
Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Xu, C.Y.[Chun-Yan], Li, C.Z.[Cheng-Zheng], Cui, Z.[Zhen], Zhang, T.[Tong], Yang, J.[Jian],
Hierarchical Semantic Propagation for Object Detection in Remote Sensing Imagery,
GeoRS(58), No. 6, June 2020, pp. 4353-4364.
IEEE DOI 2005
Hierarchical semantic propagation (HSP), object detection, remote sensing imagery BibRef

Li, C.Z.[Cheng-Zheng], Xu, C.Y.[Chun-Yan], Cui, Z.[Zhen], Wang, D.[Dan], Zhang, T.[Tong], Yang, J.[Jian],
Feature-Attentioned Object Detection in Remote Sensing Imagery,
ICIP19(3886-3890)
IEEE DOI 1910
Object detection, remote sensing, aerial and satellite imagery, feature attention BibRef

Koga, Y.[Yohei], Miyazaki, H.[Hiroyuki], Shibasaki, R.[Ryosuke],
A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation,
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CirSysVideo(30), No. 6, June 2020, pp. 1758-1770.
IEEE DOI 2006
Object detection, Feature extraction, Detectors, Unmanned aerial vehicles, Deconvolution, Photography, spatial context analysis BibRef

Du, D.W.[Da-Wei], Qi, Y.K.[Yuan-Kai], Yu, H.Y.[Hong-Yang], Yang, Y.F.[Yi-Fan], Duan, K.W.[Kai-Wen], Li, G.R.[Guo-Rong], Zhang, W.G.[Wei-Gang], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
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Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks,
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Multi-source, Vehicle detection, Optical remote sensing imagery, Fine-tuning, Segmentation, Active classification network BibRef

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Gu, Y., Wang, T., Jin, X., Gao, G.,
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Satellites, Feature extraction, Trajectory, Data mining, Event detection, Automobiles, Training, Event detection, video understanding BibRef

Sun, P.[Peng], Zheng, Y.B.[Yong-Bin], Zhou, Z.T.[Zong-Tan], Xu, W.Y.[Wan-Ying], Ren, Q.A.[Qi-Ang],
R4 Det: Refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images,
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Rotating object detection in aerial images and videos, Single-stage detector, A novel encoder-decoder architecture, Feature refinement BibRef

Li, J.[Jie], Zhang, S.[Sheng], Han, K.[Kai], Yuan, X.[Xia], Zhao, C.X.[Chun-Xia], Liu, Y.[Yu],
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Object detection, Deep learning, Data generation, Multi-source learning BibRef

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Shen, J.Q.[Jia-Quan], Zhou, W.C.[Wang-Cheng], Liu, N.Z.[Ning-Zhong], Sun, H.[Han], Li, D.[Deguang], Zhang, Y.X.[Yong-Xin],
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Feature extraction, Object detection, Computational modeling, Convolution, Training, Detection algorithms, Predictive models, lightweight convolution network BibRef

Ming, Q.[Qi], Miao, L.J.[Ling-Juan], Zhou, Z.Q.[Zhi-Qiang], Song, J.J.[Jun-Jie], Yang, X.[Xue],
Sparse Label Assignment for Oriented Object Detection in Aerial Images,
RS(13), No. 14, 2021, pp. xx-yy.
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Aircraft, ships, etc. BibRef

Dong, Y.P.[Yun-Peng], Xie, X.Z.[Xiao-Zhu], An, Z.[Zhe], Qu, Z.Y.[Zhi-Yu], Miao, L.J.[Ling-Juan], Zhou, Z.Q.[Zhi-Qiang],
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Li, X.[Xungen], Men, F.F.[Fei-Fei], Lv, S.S.[Shuai-Shuai], Jiang, X.[Xiao], Pan, M.[Mian], Ma, Q.[Qi], Yu, H.B.[Hai-Bin],
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Liu, E.[Enhai], Zheng, Y.[Yu], Pan, B.[Bin], Xu, X.[Xia], Shi, Z.W.[Zhen-Wei],
DCL-Net: Augmenting the Capability of Classification and Localization for Remote Sensing Object Detection,
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IEEE DOI 2109
Convolution, Location awareness, Remote sensing, Feature extraction, Kernel, Object detection, Couplings, remote sensing object detection BibRef

Zhang, R.Q.[Rui-Qian], Newsam, S.[Shawn], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Wang, J.M.[Jia-Ming], Li, D.R.[De-Ren],
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Elsevier DOI 2109
Vehicle detection, UAV imagery, Multi-scale structure, Adversarial network, Domain adaptation BibRef

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Yu, L.J.[Li-Jian], Zhi, X.[Xiyang], Hu, J.M.[Jian-Ming], Jiang, S.[Shikai], Zhang, W.[Wei], Chen, W.B.[Wen-Bin],
Small-Sized Vehicle Detection in Remote Sensing Image Based on Keypoint Detection,
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Sun, M.Y.[Ming-Yuan], Zhang, H.C.[Hao-Chun], Huang, Z.L.[Zi-Liang], Luo, Y.Q.[Yue-Qi], Li, Y.Y.[Yi-Yi],
Road infrared target detection with I-YOLO,
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deep learning, image processing, infrared image, target detection BibRef

Yao, X.[Xin], Shi, X.R.[Xiao-Ran], Li, Y.X.[Ya-Xin], Wang, L.[Li], Wang, H.[Han], Ren, S.J.[Shi-Jie], Zhou, F.[Feng],
GMT-WGAN: An Adversarial Sample Expansion Method for Ground Moving Targets Classification,
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Liu, J.H.[Jia-Hang], Yang, D.H.[Dong-Hao], Hu, F.[Fei],
Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention,
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Li, Q.Y.[Qing-Yun], Chen, Y.S.[Yu-Shi], Zeng, Y.[Ying],
Transformer with Transfer CNN for Remote-Sensing-Image Object Detection,
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Chalavadi, V.[Vishnu], Jeripothula, P.[Prudviraj], Datla, R.[Rajeshreddy], Babu, C.S.[C. Sobhan], Mohan, C.K.[C. Krishna],
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Multi-scale object detection, Contextual features, Dilated convolutions, Aerial images BibRef

Zhou, L.M.[Li-Ming], Rao, X.H.[Xiao-Han], Li, Y.H.[Ya-Hui], Zuo, X.Y.[Xian-Yu], Qiao, B.J.[Bao-Jun], Lin, Y.H.[Ying-Hao],
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Liu, F.F.[Fan-Fan], Zhao, W.Z.[Wen-Zhe], Zhou, G.Y.[Guang-Yao], Zhao, L.J.[Liang-Jin], Wei, H.R.[Hao-Ran],
SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images,
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Zhu, S.J.[Sheng-Jie], Liu, J.H.[Jing-Hong], Tian, Y.[Yang], Zuo, Y.J.[Yu-Jia], Liu, C.L.[Cheng-Long],
Rapid Vehicle Detection in Aerial Images under the Complex Background of Dense Urban Areas,
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Guo, Y.[Yinong], Wu, C.[Chen], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
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Elsevier DOI 2206
Vehicle Counting, Density Estimation, Remote Sensing, CNNs, GF-2 BibRef

Zhu, X.[Xuan], Liang, B.B.[Bin-Bin], Fu, D.[Daoyong], Huang, G.X.[Guo-Xin], Yang, F.[Fan], Li, W.[Wei],
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Lê, H.Â.[Hoàng-Ân], Zhang, H.[Heng], Pham, M.T.[Minh-Tan], Lefèvre, S.[Sébastien],
Mutual Guidance Meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
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Zou, M.L.[Mu-Lan], Jin, G.H.[Guang-Hu], Li, L.[Liang], He, Z.H.[Zhi-Hua],
A Robust Dual-Platform GMTI Method against Nonuniform Clutter,
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Ni, H.[Huan], Chanussot, J.[Jocelyn], Niu, X.N.[Xiao-Nan], Tang, H.[Hong], Guan, H.Y.[Hai-Yan],
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IJGI(11), No. 9, 2022, pp. xx-yy.
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Ma, W.X.[Wei-Xuan], Zhu, S.[Sa],
A Multifeature-Assisted Road and Vehicle Detection Method Based on Monocular Depth Estimation and Refined U-V Disparity Mapping,
ITS(23), No. 9, September 2022, pp. 16763-16772.
IEEE DOI 2209
Roads, Estimation, Vehicle detection, Feature extraction, Cameras, Detection algorithms, Computational modeling, Deep learning, road and vehicle detection BibRef

Sun, Y.M.[Yi-Ming], Cao, B.[Bing], Zhu, P.F.[Peng-Fei], Hu, Q.H.[Qing-Hua],
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CirSysVideo(32), No. 10, October 2022, pp. 6700-6713.
IEEE DOI 2210
Vehicle detection, Object detection, Uncertainty, Drones, Surveillance, Lighting, Sun, Cross-modality, uncertainty-aware BibRef

Stateczny, A.[Andrzej], Kiran, G.U.[Goru Uday], Bindu, G.[Garikapati], Chythanya, K.R.[Kanegonda Ravi], Swamy, K.A.[Kondru Ayyappa],
Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection,
RS(14), No. 21, 2022, pp. xx-yy.
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Lang, K.Q.[Kai-Qi], Yang, M.Y.[Ming-Yu], Wang, H.[Hao], Wang, H.Y.[Han-Yu], Wang, Z.L.[Zi-Long], Zhang, J.Z.[Jing-Zhong], Shen, H.H.[Hong-Hai],
Improved One-Stage Detectors with Neck Attention Block for Object Detection in Remote Sensing,
RS(14), No. 22, 2022, pp. xx-yy.
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Ge, Z.[Zuhao], Qi, L.[Lizhe], Wang, Y.Z.[Yu-Zheng], Sun, Y.Q.[Yun-Quan],
Zoom-and-Reasoning: Joint Foreground Zoom and Visual-Semantic Reasoning Detection Network for Aerial Images,
SPLetters(29), 2022, pp. 2572-2576.
IEEE DOI 2301
Cognition, Detectors, Visualization, Semantics, Feature extraction, Object detection, Pipelines, Aerial object detection, graph convolutional network BibRef

Chen, R.[Renxi], Ferreira, V.G.[Vagner G.], Li, X.H.[Xin-Hui],
Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Hou, L.P.[Li-Ping], Lu, K.[Ke], Yang, X.[Xue], Li, Y.Q.[Yu-Qiu], Xue, J.[Jian],
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Feng, J.[Jie], Liang, Y.P.[Yu-Ping], Zhang, X.R.[Xiang-Rong], Zhang, J.P.[Jun-Peng], Jiao, L.C.[Li-Cheng],
SDANet: Semantic-Embedded Density Adaptive Network for Moving Vehicle Detection in Satellite Videos,
IP(32), 2023, pp. 1788-1801.
IEEE DOI 2303
Videos, Satellites, Detectors, Feature extraction, Object detection, Roads, Kernel, Anchor-free detector, moving object detection, weakly supervised learning BibRef

Kong, X.H.[Xiang-Hui], Zhang, Y.[Yan], Tu, S.T.[Shang-Tan], Xu, C.[Chang], Yang, W.[Wen],
Vehicle Detection in High-Resolution Aerial Images with Parallel RPN and Density-Assigner,
RS(15), No. 6, 2023, pp. 1659.
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Zhang, H.H.[Hai-Han], Xie, C.[Chun], Toriya, H.[Hisatoshi], Shishido, H.[Hidehiko], Kitahara, I.[Itaru],
Vehicle Localization in a Completed City-Scale 3D Scene Using Aerial Images and an On-Board Stereo Camera,
RS(15), No. 15, 2023, pp. xx-yy.
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Chen, L.[Lulu], Zhao, Y.Q.[Yong-Qiang], Kong, S.G.[Seong G.],
SFA-guided mosaic transformer for tracking small objects in snapshot spectral imaging,
PandRS(204), 2023, pp. 223-236.
Elsevier DOI 2310
Small object tracking, Snapshot spectral imaging, Spectral filter array, Mosaic transformer, Multi-layer feature aggregation BibRef

Alajmi, M.[Masoud], Alamro, H.[Hayam], Al-Mutiri, F.[Fuad], Aljebreen, M.[Mohammed], Othman, K.M.[Kamal M.], Sayed, A.[Ahmed],
Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique,
RS(15), No. 18, 2023, pp. 4600.
DOI Link 2310
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Zhu, H.[Hong], Lv, Y.[Yanan], Meng, J.[Jian], Liu, Y.X.[Yu-Xuan], Hu, L.[Liuru], Yao, J.Q.[Jia-Qi], Lu, X.H.X.[Xiong-Han-Xuan],
Vehicle Detection in Multisource Remote Sensing Images Based on Edge-Preserving Super-Resolution Reconstruction,
RS(15), No. 17, 2023, pp. 4281.
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Wang, X.B.[Xia-Bin], Yan, Y.[Ye], Sun, H.[Haohui], Zhu, D.[Dekang],
Dense-and-Similar Object detection in aerial images,
PRL(176), 2023, pp. 153-159.
Elsevier DOI 2312
Dense and small object, Similar object, Confusion categories, Object detection, Aerial images BibRef

Sheehan, A.[Annalisa], Beddows, A.[Andrew], Green, D.C.[David C.], Beevers, S.[Sean],
City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona,
RS(15), No. 24, 2023, pp. 5709.
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Zhang, G.Q.[Guo-Qing], Zheng, C.[Chen], Ye, Z.[Zhonglin],
Transformer-Based Feature Compensation Network for Aerial Photography Person and Ground Object Recognition,
RS(16), No. 2, 2024, pp. 268.
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Guan, X.[Xin], Dong, Y.F.[Yi-Fan], Tan, W.X.[Wei-Xian], Su, Y.[Yun], Huang, P.P.[Ping-Ping],
A Parameter-Free Pixel Correlation-Based Attention Module for Remote Sensing Object Detection,
RS(16), No. 2, 2024, pp. 312.
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Ying, Z.[Zilu], Zhou, J.H.[Jian-Hong], Zhai, Y.[Yikui], Quan, H.[Hao], Li, W.[Wenba], Genovese, A.[Angelo], Piuri, V.[Vincenzo], Scotti, F.[Fabio],
Large-Scale High-Altitude UAV-Based Vehicle Detection via Pyramid Dual Pooling Attention Path Aggregation Network,
ITS(25), No. 10, October 2024, pp. 14426-14444.
IEEE DOI Code:
WWW Link. 2410
Vehicle detection, Autonomous aerial vehicles, Meters, Feature extraction, Task analysis, Urban areas, Spatial resolution, attention mechanism BibRef

Yu, C.R.[Cheng-Rui], Jiang, X.N.[Xiao-Nan], Wu, F.[Fanlu], Fu, Y.[Yao], Pei, J.[Junyan], Zhang, Y.[Yu], Li, X.Z.[Xiang-Zhi], Fu, T.J.[Tian-Jiao],
A Multi-Scale Feature Fusion Based Lightweight Vehicle Target Detection Network on Aerial Optical Images,
RS(16), No. 19, 2024, pp. 3637.
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Wang, Z.Y.[Zi-Yang], Liu, Z.W.[Zhi-Wen], Xu, G.[Gen], Cheng, S.Y.[Si-Yuan],
Object Detection in UAV Aerial Images Based on Improved YOLOv7-tiny,
CVIDL23(370-374)
IEEE DOI 2403
Deep learning, Head, Object detection, Interference, Autonomous aerial vehicles, Feature extraction, Transformers, feature fusion BibRef

Wang, N.[Ning], Jia, Y.[Yinshan],
Research on Vehicle Object Detection Based on Deep Learning,
CVIDL23(412-415)
IEEE DOI 2403
Deep learning, Performance evaluation, Convolution, Computational modeling, Neural networks, Object detection, YOLO BibRef

Liu, S.[Shuo], Zou, H.X.[Huan-Xin], Huang, Y.[Yazhe], Cao, X.[Xu], He, S.T.[Shi-Tian], Li, M.L.[Mei-Lin], Zhang, Y.Q.[Yu-Qing],
ERF-RTMDet: An Improved Small Object Detection Method in Remote Sensing Images,
RS(15), No. 23, 2023, pp. 5575.
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Negin, F.[Farhood], Tabejamaat, M.[Mohsen], Fraisse, R.[Renaud], Bremond, F.[Francois],
Transforming Temporal Embeddings to Keypoint Heatmaps for Detection of Tiny Vehicles in Wide Area Motion Imagery (WAMI) Sequences,
EarthVision22(1431-1440)
IEEE DOI 2210
Heating systems, Shape, Object detection, Detectors, Gaussian distribution, Transformers, Search problems BibRef

Wang, S.[Shan], Zhang, Y.[Yanhao], Perincherry, A.[Akhil], Vora, A.[Ankit], Li, H.D.[Hong-Dong],
View Consistent Purification for Accurate Cross-View Localization,
ICCV23(8163-8172)
IEEE DOI 2401
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Xia, Z.M.[Zi-Min], Shi, Y.[Yujiao], Li, H.D.[Hong-Dong], Kooij, J.F.P.[Julian F. P.],
Adapting Fine-grained Cross-view Localization to Areas Without Fine Ground Truth,
ECCV24(XXXI: 397-415).
Springer DOI 2412
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Wang, S.[Shan], Nguyen, C.[Chuong], Liu, J.W.[Jia-Wei], Zhang, Y.[Yanhao], Muthu, S.[Sundaram], Maken, F.A.[Fahira Afzal], Zhang, K.[Kaihao], Li, H.D.[Hong-Dong],
View from Above: Orthogonal-View Aware Cross-View Localization,
CVPR24(14843-14852)
IEEE DOI Code:
WWW Link. 2410
Location awareness, Visualization, Simultaneous localization and mapping, Image databases, Domain alignment BibRef

Shi, Y.J.[Yu-Jiao], Yu, X.[Xin], Wang, S.[Shan], Li, H.D.[Hong-Dong],
Cvlnet: Cross-view Semantic Correspondence Learning for Video-based Camera Localization,
ACCV22(I:123-141).
Springer DOI 2307
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Shi, Y.J.[Yu-Jiao], Li, H.D.[Hong-Dong],
Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image,
CVPR22(16989-16999)
IEEE DOI 2210
Location awareness, Geometry, Tracking loops, Satellites, Simultaneous localization and mapping, Image retrieval, Robot vision BibRef

Makrigiorgis, R.[Rafael], Hadjittoouli, N.[Nicolas], Kyrkou, C.[Christos], Theocharides, T.[Theocharis],
AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring,
WACV22(3431-3440)
IEEE DOI 2202
Image segmentation, Roads, Vehicle detection, Urban areas, Pipelines, Streaming media, Autonomous aerial vehicles, Vision Systems and Applications BibRef

Yu, D.Y.[Da-Yang], Zhang, R.[Rong], Qin, S.[Shan],
Cascade Saliency Attention Network for Object Detection in Remote Sensing Images,
ICPR21(217-223)
IEEE DOI 2105
Image segmentation, Merging, Interference, Object detection, Detectors, Task analysis BibRef

Chung, H.S.[Hyun-Seung], Nam, W.J.[Woo-Jeoung], Lee, S.W.[Seong-Whan],
Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning,
ICPR21(6431-6438)
IEEE DOI 2105
Measurement, Training, Visualization, Convolution, Databases, Image retrieval, Merging BibRef

Xia, G.S.[Gui-Song], Bai, X.[Xiang], Ding, J.[Jian], Zhu, Z.[Zhen], Belongie, S.[Serge], Luo, J.B.[Jie-Bo], Datcu, M.[Mihai], Pelillo, M.[Marcello], Zhang, L.P.[Liang-Pei],
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images,
CVPR18(3974-3983)
IEEE DOI
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Dataset, Vehicle Detection.
WWW Link. Object detection, Earth, Sports, Sensors, Marine vehicles, Image sensors BibRef

Kumdakci, H.[Hilmi], Öngün, C.[Cihan], Temizel, A.[Alptekin],
Generative Data Augmentation for Vehicle Detection in Aerial Images,
WAAMI20(19-31).
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Gao, H.B.[Hao-Bo], Li, X.[Xin],
Vehicle Detection in High Resolution Image Based on Deep Learning,
ISPRS20(B3:49-54).
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Song, W., Li, S., Chang, T., Hao, A., Zhao, Q., Qin, H.,
Cross-View Contextual Relation Transferred Network for Unsupervised Vehicle Tracking in Drone Videos,
WACV20(1696-1705)
IEEE DOI 2006
Target tracking, Videos, Task analysis, Drones, Context modeling, Training BibRef

Zhang, X.D.[Xin-Di], Izquierdo, E.[Ebroul], Chandramouli, K.[Krishna],
Dense and Small Object Detection in UAV Vision Based on Cascade Network,
VisDrone19(118-126)
IEEE DOI 2004
Infrastructure maintenance. autonomous aerial vehicles, mobile robots, object detection, remotely operated vehicles, robot vision, surveillance, UAV BibRef

Pi, Z., Lian, Y., Chen, X., Wu, Y., Li, Y., Jiao, L.,
A Novel Spatial and Temporal Context-Aware Approach for Drone-Based Video Object Detection,
VisDrone19(179-188)
IEEE DOI 2004
autonomous aerial vehicles, object detection, object tracking, remotely operated vehicles, video signal processing, tracking BibRef

Bahmanyar, R., Azimi, S.M., Reinartz, P.,
Multiple Vehicles and People Tracking in Aerial Imagery Using Stack of Micro Single-Object-Tracking CNNs,
SMPR19(163-170).
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Mandal, M., Shah, M., Meena, P., Vipparthi, S.K.,
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes,
ICIP19(3098-3102)
IEEE DOI 1910
aerial scene, vehicle detection, deep learning, real-time, remote sensing BibRef

Azimi, S.M.[Seyed Majid], Vig, E.[Eleonora], Bahmanyar, R.[Reza], Körner, M.[Marco], Reinartz, P.[Peter],
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery,
ACCV18(III:150-165).
Springer DOI 1906
BibRef

Azimi, S.M.[Seyed Majid],
ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery,
CVUAV18(II:88-99).
Springer DOI 1905
BibRef

Krajewski, R., Moers, T., Eckstein, L.,
VeGAN: Using GANs for Augmentation in Latent Space to Improve the Semantic Segmentation of Vehicles in Images From an Aerial Perspective,
WACV19(1440-1448)
IEEE DOI 1904
autonomous aerial vehicles, image representation, image sampling, image segmentation, neural nets, object detection, Training data BibRef

Dhawale, A., Shankar, K.S., Michael, N.,
Fast Monte-Carlo Localization on Aerial Vehicles Using Approximate Continuous Belief Representations,
CVPR18(5851-5859)
IEEE DOI 1812
Cameras, Robot sensing systems, Real-time systems, Computational modeling, Pose estimation BibRef

Schulter, S.[Samuel], Zhai, M.[Menghua], Jacobs, N.[Nathan], Chandraker, M.[Manmohan],
Learning to Look around Objects for Top-View Representations of Outdoor Scenes,
ECCV18(XV: 815-831).
Springer DOI 1810
BibRef

Tan, Y., Xu, Y., Das, S., Chaudhry, A.,
Vehicle Detection and Classification in Aerial Imagery,
ICIP18(86-90)
IEEE DOI 1809
Vehicle detection, Proposals, Heating systems, Training, Feature extraction, Noise measurement, Streaming media, CNN BibRef

Yang, M.Y.[Michael Ying], Liao, W.T.[Wen-Tong], Li, X.B.[Xin-Bo], Rosenhahn, B.[Bodo],
Deep Learning for Vehicle Detection in Aerial Images,
ICIP18(3079-3083)
IEEE DOI 1809
Proposals, Feature extraction, Training, Vehicle detection, Detectors, Entropy, Convolutional neural networks, ITCVD dataset BibRef

Zhao, J.[Jiao], Han, J.[Jing], Feng, C.[Chen], Yao, J.[Jian],
A Systematic Scheme for Automatic Airplane Detection from High-Resolution Remote Sensing Images,
PSIVTWS17(465-478).
Springer DOI 1806
BibRef

Sommer, L., Schmidt, N., Schumann, A., Beyerer, J.,
Search Area Reduction Fast-RCNN for Fast Vehicle Detection in Large Aerial Imagery,
ICIP18(3054-3058)
IEEE DOI 1809
Proposals, Vehicle detection, Feature extraction, Object detection, Training, Machine learning, Search problems, Object Detection, Aerial Imagery BibRef

Sommer, L., Nie, K., Schumann, A., Schuchert, T., Beyerer, J.,
Semantic labeling for improved vehicle detection in aerial imagery,
AVSS17(1-6)
IEEE DOI 1806
BibRef
And: A2, A1, A3, A5, Only:
Semantic Labeling Based Vehicle Detection in Aerial Imagery,
WACV18(626-634)
IEEE DOI 1806
learning (artificial intelligence), object detection, traffic engineering computing, aerial imagery, deep learning, Vegetation. disasters, image representation, Faster R-CNN, Task analysis BibRef

Poostchi, M., Palaniappan, K., Seetharaman, G.,
Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery,
AVSS17(1-6)
IEEE DOI 1806
image colour analysis, image fusion, image motion analysis, object detection, object tracking, target tracking, Visualization BibRef

Terrail, J.O.D., Jurie, F.,
On the use of deep neural networks for the detection of small vehicles in ortho-images,
ICIP17(4212-4216)
IEEE DOI 1803
Automobiles, Benchmark testing, Heating systems, Image resolution, Proposals, Task analysis, Training, aerial-imagery, deep-learning, target detection BibRef

Logoglu, K.B., Lezki, H., Yucel, M.K., Ozturk, A., Kucukkomurler, A., Karagoz, B., Erdem, A., Erdem, E.,
Feature-Based Efficient Moving Object Detection for Low-Altitude Aerial Platforms,
CVUAV17(2119-2128)
IEEE DOI 1802
Cameras, Graphics processing units, Object detection, Optical imaging, Pipelines, Real-time systems, Unmanned aerial vehicles BibRef

Dahmane, M., Foucher, S., Beaulieu, M., Bouroubi, Y., Benoit, M.,
The Potential of Deep Features for Small Object Class Identification in Very High Resolution Remote Sensing Imagery,
ICIAR17(569-577).
Springer DOI 1706
Cars in very high resolution Pleiades imagery. BibRef

Kamenetsky, D., Sherrah, J.[Jamie],
Aerial Car Detection and Urban Understanding,
DICTA15(1-8)
IEEE DOI 1603
Fourier analysis
See also Pedestrian Multiple Hypothesis Tracker Fusing Head and Body Detections, A. BibRef

Papachristos, C.[Christos], Tzoumanikas, D.[Dimos], Alexis, K.[Kostas], Tzes, A.[Anthony],
Autonomous Robotic Aerial Tracking, Avoidance, and Seeking of a Mobile Human Subject,
ISVC15(I: 444-454).
Springer DOI 1601
BibRef

Arandjelovic, O.,
Automatic vehicle tracking and recognition from aerial image sequences,
AVSS15(1-6)
IEEE DOI 1511
image sequences BibRef

Teutsch, M.[Michael], Kruger, W.[Wolfgang],
Robust and fast detection of moving vehicles in aerial videos using sliding windows,
CVVT15(26-34)
IEEE DOI 1510
Cameras BibRef

Fehlmann, S., Pontecorvo, C., Booth, D.M., Janney, P., Christie, R., Redding, N.J., Royce, M., Fiebig, M.,
Fusion of Multiple Sensor Data to Recognise Moving Objects in Wide Area Motion Imagery,
DICTA14(1-8)
IEEE DOI 1502
Based on vehicle recognition. image fusion BibRef

Desai, A.[Alok], Lee, D.J.[Dah-Jye], Zhang, M.[Meng],
Using Accurate Feature Matching for Unmanned Aerial Vehicle Ground Object Tracking,
ISVC14(I: 435-444).
Springer DOI 1501
BibRef

Bhaskar, H.[Harish], Dias, J.[Jorge], Seneviratne, L.[Lakmal], Al-Mualla, M.[Mohammed],
micro-UAV Based Dynamic Target Tracking for Surveillance and Exploration,
ISVC14(I: 445-456).
Springer DOI 1501
BibRef

Climent-Pérez, P.[Pau], Lazaridis, G.[Georgios], Hummel, G.[Georg], Russ, M.[Martin], Monekosso, D.N.[Dorothy N.], Remagnino, P.[Paolo],
Telemetry-Based Search Window Correction for Airborne Tracking,
ISVC14(I: 457-466).
Springer DOI 1501
BibRef

Cruz, G.[Gonçalo], Bernardino, A.[Alexandre],
Image Saliency Applied to Infrared Images for Unmanned Maritime Monitoring,
CVS15(511-522).
Springer DOI 1507
BibRef

Marques, J.S.[Jorge S.], Bernardino, A.[Alexandre], Cruz, G.[Goncalo], Bento, M.[Maria],
An algorithm for the detection of vessels in aerial images,
AVSS14(295-300)
IEEE DOI 1411
Cameras BibRef

Teutsch, M.[Michael], Kruger, W.[Wolfgang], Beyerer, J.[Jurgen],
Evaluation of object segmentation to improve moving vehicle detection in aerial videos,
AVSS14(265-270)
IEEE DOI 1411
Feature extraction BibRef

Chen, X.Y.[Xue-Yun], Xiang, S.M.[Shi-Ming], Liu, C.L.[Cheng-Lin], Pan, C.H.[Chun-Hong],
Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks,
ACPR13(181-185)
IEEE DOI 1408
convolution BibRef

Matzen, K.[Kevin], Snavely, N.[Noah],
NYC3DCars: A Dataset of 3D Vehicles in Geographic Context,
ICCV13(761-768)
IEEE DOI 1403
Dataset, Vehicles. 3D models; geography; object detection; structure from motion BibRef

Leister, W., Tuermer, S., Reinartz, P., Hoffmann, K.H., Stilla, U.,
Validation of Vehicle Candidate Areas in Aerial Images Using Color Co-Occurrence Histograms,
SSG13(139-144).
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Krüger, T., Nowak, S., Matthaei, J., Bestmann, U.,
Single-Layer Laser Scanner for Detection and Localization of Unmanned Swarm Members,
UAV-g13(229-234).
DOI Link 1311
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Máttyus, G.,
Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images,
Hannover13(233-237).
DOI Link 1308
BibRef

Chan, M.T., Weed, C.,
Vessel detection in video with dynamic maritime background,
AIPR12(1-5)
IEEE DOI 1307
geometry BibRef

Frias-Velazquez, A.[Andres], Ortiz, C.[Carlos], Pizurica, A.[Aleksandra], Philips, W.[Wilfried], Cerda, G.[Gustavo],
Object identification by using orthonormal circus functions from the trace transform,
ICIP12(2153-2156).
IEEE DOI 1302
salient information from trace transform signatures. BibRef

Chen, L.[Long], Jiang, Z.G.[Zhi-Guo], Yang, J.L.[Jun-Li], Ma, Y.B.[Yi-Bing],
A coarse-to-fine approach for vehicles detection from aerial images,
CVRS12(221-225).
IEEE DOI 1302
BibRef

Zhou, M., Tang, L.L., Li, C.R., Peng, Z., Li, J.M.,
A Recognition Method for Airplane Targets Using 3D Point Cloud Data,
ISPRS12(XXXIX-B3:199-203).
DOI Link 1209
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Wei, Y., Yao, W., Wu, J., Schmitt, M., Stilla, U.,
Adaboost-based Feature Relevance Assessment in Fusing LIDAR and Image Data for Classification of Trees and Vehicles in Urban Scenes,
AnnalsPRS(I-7), No. 2012, pp. 323-328.
DOI Link 1209
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Mishra, R.K.,
Automatic Moving Vehicle's Information Extraction From One-pass Worldview-2 Satellite Imagery,
ISPRS12(XXXIX-B7:323-328).
DOI Link 1209
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Guilmart, C., Herbin, S., Perez, P.,
Context-driven moving object detection in aerial scenes with user input,
ICIP11(1781-1784).
IEEE DOI 1201
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Li, W.[Wei], Xiang, S.M.[Shi-Ming], Wang, H.B.[Hai-Bo], Pan, C.H.[Chun-Hong],
Robust airplane detection in satellite images,
ICIP11(2821-2824).
IEEE DOI 1201
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Sadlier, D.A., Ferguson, P., Conaire, C.O., O'Connor, N.E., Doyle, K.,
Image-based vehicle indexing for a seaport transportation surveillance system,
AVSBS11(367-372).
IEEE DOI 1111
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Diegert, C.[Carl],
A combinatorial method for tracing objects using semantics of their shape,
AIPR10(1-4).
IEEE DOI 1010
Vehicles in Overhead Imaging Research Dataset BibRef

Yang, B.[Bo], Sharma, P.[Pramod], Nevatia, R.[Ram],
Vehicle detection from low quality aerial LIDAR data,
WACV11(541-548).
IEEE DOI 1101

See also Efficient incremental learning of boosted classifiers for object detection. BibRef

Artese, G.[Giuseppe],
Improvement of a procedure for vehicle detection and tracking by base frame updating and Kalman filter,
PCVIA10(A:55).
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Tuermer, S.[Sebastian], Leitloff, J.[Jens], Reinartz, P.[Peter], Stilla, U.[Uwe],
Evaluation of selected features for car detection in aerial images,
HighRes11(xx-yy).
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Earlier:
Automatic vehicle detection in aerial image sequences of urban areas using 3D HoG features,
PCVIA10(B:50).
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Ozcanli, O.C.[Ozge Can], Mundy, J.L.[Joseph L.],
Vehicle Recognition as Changes in Satellite Imagery,
ICPR10(3336-3339).
IEEE DOI 1008
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Khan, S.M.[Saad M.], Cheng, H.[Hui], Matthies, D.[Dennis], Sawhney, H.S.[Harpreet S.],
3D model based vehicle classification in aerial imagery,
CVPR10(1681-1687).
IEEE DOI 1006
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Xiao, J.J.[Jiang-Jian], Cheng, H.[Hui], Sawhney, H.S.[Harpreet S.], Han, F.[Feng],
Vehicle detection and tracking in wide field-of-view aerial video,
CVPR10(679-684).
IEEE DOI 1006

See also Robust Video Georegistration. BibRef

Tolba, H., Elgerzawy, A.,
Comparative Experiments to Evaluate a CHMM-Based Identification Approach to Naval Targets,
WSSIP09(1-4).
IEEE DOI 0906

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Kuo, C.H.[Cheng-Hao], Nevatia, R.[Ramakant],
Robust multi-view car detection using unsupervised sub-categorization,
WACV09(1-8).
IEEE DOI 0912
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Prokaj, J.[Jan], Medioni, G.[Gerard],
3-D model based vehicle recognition,
WACV09(1-7).
IEEE DOI 0912
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Hutter, M., Brewer, N.,
Matching 2-D ellipses to 3-D circles with application to vehicle pose identification,
IVCNZ09(153-158).
IEEE DOI 0911
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Perconti, P., Hilger, J., Loew, M.,
Vehicle detection approaches using the NVESD Sensor Fusion Testbed,
AIPR03(56-61).
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Wheeler, F.W., Hoogs, A.J.,
Moving Vehicle Registration and Super-Resolution,
AIPR07(101-107).
IEEE DOI 0710
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Tongphu, S.[Suwan], Thongsak, N.[Naddao], Dailey, M.N.[Matthew N.],
Rapid Detection of Many Object Instances,
ACIVS09(434-444).
Springer DOI 0909
Car detection with small training set. Sliding window using discrete features of window. BibRef

Choi, J.Y.[Jae-Young], Yang, Y.K.[Young-Kyu],
Vehicle Detection from Aerial Images Using Local Shape Information,
PSIVT09(227-236).
Springer DOI 0901
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Kluckner, S.[Stefan], Pacher, G.[Georg], Grabner, H.[Helmut], Bischof, H.[Horst], Bauer, J.[Joachim],
A 3D Teacher for Car Detection in Aerial Images,
ICCV07(1-8).
IEEE DOI 0710
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Auclair, A.[Adrien], Cohen, L.D.[Laurent D.], Vincent, N.[Nicole],
A Robust Approach for 3D Cars Reconstruction,
SCIA07(183-192).
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Piriyakumar Douglas, A.L., Prasad, M., Sunil Gowtham, B., Kalyansundar, A., Swaminathan, V., Chattopadhyay, R.,
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ICIP06(3273-3276).
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Hinz, S.[Stefan], Wiedemann, C.[Christian],
Vehicle Detection in Aerial Images Using Generic Features, Grouping, and Context,
DAGM01(xx-yy).
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Hermiston, K., Booth, D.M.,
Pose Estimation and Recognition of Ground Vehicles in Aerial Reconnaissance Imagery,
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Booth, D.M., Radford, C.J.,
The detection of vehicles in airborne downward looking infrared linescan imagery,
ICPR94(A:769-772).
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Ruskone, R., Guigues, L., Airault, S., Jamet, O.,
Vehicle Detection on Aerial Images: A Structural Approach,
ICPR96(III: 900-904).
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Rapid Search for Spherical Objects in Aerial Photographs,
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Automatic Detection of Vehicles in Aerial Photographs of Highways,
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
ATR - Oriented Objects, Vehicles, Aerial Images .