16.7.2 Vehicle Motion Understanding and Analysis

Suhr, J.K., Jang, J., Min, D., Jung, H.G.,
Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments,
ITS(18), No. 5, May 2017, pp. 1078-1086.
IEEE DOI 1705
Cameras, Feature extraction, Global Positioning System, Roads, Urban areas, Vehicles, Wheels, GPS, IMU, Vehicle localization, digital map, particle filter, road marking, sensor, fusion BibRef

Yuan, X., Cao, X., Hao, X., Chen, H., Wei, X.,
Vehicle Detection by a Context-Aware Multichannel Feature Pyramid,
SMCS(47), No. 7, July 2017, pp. 1348-1357.
IEEE DOI 1706
Cameras, Feature extraction, Gray-scale, Image color analysis, Transforms, Vehicle detection, Vehicles, Context-aware feature, context-aware structural feature, scale invariant, vehicle, detection BibRef

Belgiovane, D.J., Chen, C.C., Chien, S.Y.P., Sherony, R.,
Surrogate Bicycle Design for Millimeter-Wave Automotive Radar Pre-Collision Testing,
ITS(18), No. 9, September 2017, pp. 2413-2422.
IEEE DOI 1709
bicycles, road safety, precollision systems, automotive radar, collision avoidance, pedestrian detection BibRef

Aqel, S., Hmimid, A., Sabri, M.A., Aarab, A.,
Road traffic: Vehicle detection and classification,
ISCV17(1-5)
IEEE DOI 1710
image motion analysis, object detection, road traffic, background subtraction approach, Shadow detection and removal. BibRef

Zhuo, L.[Li], Jiang, L.Y.[Li-Ying], Zhu, Z.Q.[Zi-Qi], Li, J.F.[Jia-Feng], Zhang, J.[Jing], Long, H.X.[Hai-Xia],
Vehicle classification for large-scale traffic surveillance videos using Convolutional Neural Networks,
MVA(28), No. 7, October 2017, pp. 793-802.
Springer DOI 1710
BibRef

Liu, M.[Meng], Hua, W.[Wang], Wei, Q.[Quan],
Vehicle detection using three-axis AMR sensors deployed along travel lane markings,
IET-ITS(11), No. 9, November 2017, pp. 581-587.
DOI Link 1710
BibRef

Chu, W., Liu, Y., Shen, C., Cai, D., Hua, X.S.,
Multi-Task Vehicle Detection With Region-of-Interest Voting,
IP(27), No. 1, January 2018, pp. 432-441.
IEEE DOI 1712
feedforward neural nets, learning (artificial intelligence), object detection, region-of-interest BibRef

Yang, Z.[Zi], Pun-Cheng, L.S.C.[Lilian S.C.],
Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,
IVC(69), 2018, pp. 143-154.
Elsevier DOI 1802
Survey, Vehicle Detection. Vehicle detection, Intelligent Transportation Systems, Varying environments, Traffic surveillance BibRef

Bai, S.[Shuang], Liu, Z.Y.[Zhen-Yao], Yao, C.[Chang],
Classify vehicles in traffic scene images with deformable part-based models,
MVA(29), No. 3, April 2018, pp. 393-403.
WWW Link. 1804
BibRef

Zhou, Y., Liu, L., Shao, L., Mellor, M.,
Fast Automatic Vehicle Annotation for Urban Traffic Surveillance,
ITS(19), No. 6, June 2018, pp. 1973-1984.
IEEE DOI 1806
Image color analysis, Proposals, Real-time systems, Surveillance, Training, Vehicle detection, Vehicle detection, latent knowledge guidance BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Smoky vehicle detection based on multi-scale block Tamura features,
SIViP(12), No. 6, September 2018, pp. 1061-1068.
WWW Link. 1808
BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Automatic smoky vehicle detection from traffic surveillance video based on vehicle rear detection and multi-feature fusion,
IET-ITS(13), No. 2, February 2019, pp. 252-259.
DOI Link 1902
BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Contour-based smoky vehicle detection from surveillance video for alarm systems,
SIViP(13), No. 2, March 2019, pp. 217-225.
WWW Link. 1904
BibRef

Chen, J., Xu, W., Xu, H., Lin, F., Sun, Y., Shi, X.,
Fast Vehicle Detection Using a Disparity Projection Method,
ITS(19), No. 9, September 2018, pp. 2801-2813.
IEEE DOI 1809
Feature extraction, Vehicle detection, Robustness, Stereo vision, Lighting, Cameras, Roads, Stereo vision, disparity feature, reusing of fourier transformation BibRef

Tu, C.L.[Chun-Ling], Du, S.Z.[Sheng-Zhi],
A Hough Space Feature for Vehicle Detection,
ISVC18(147-156).
Springer DOI 1811
BibRef

Ding, L.[Lu], Wang, Y.[Yong], Laganière, R.[Robert], Luo, X.B.[Xin-Bin], Fu, S.[Shan],
Scale-Aware RPN for Vehicle Detection,
ISVC18(487-499).
Springer DOI 1811
BibRef

Zhang, Y.S.[Yun-Sheng], Zhao, C.H.[Chi-Hang], Shi, W.[Wen], Leng, K.J.[Kai-Jun],
Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model,
IET-ITS(12), No. 10, December 2018, pp. 1283-1290.
DOI Link 1812
BibRef

OBrien, E.J.[Eugene J.], Caprani, C.C.[Colin C.], Blacoe, S.[Serena], Guo, D.[Dong], Malekjafarian, A.[Abdollah],
Detection of vehicle wheels from images using a pseudo-wavelet filter for analysis of congested traffic,
IET-IPR(12), No. 12, December 2018, pp. 2222-2228.
DOI Link 1812
BibRef

Liang, J.[Jun], Chen, X.[Xu], He, M.L.[Mei-Ling], Chen, L.[Long], Cai, T.[Tao], Zhu, N.[Ning],
Car detection and classification using cascade model,
IET-ITS(12), No. 10, December 2018, pp. 1201-1209.
DOI Link 1812
BibRef

Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., Heng, P.,
SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection,
ITS(20), No. 3, March 2019, pp. 1010-1019.
IEEE DOI 1903
Vehicle detection, Feature extraction, Proposals, Object detection, Videos, Convolutional neural networks, Computer science, intelligent transportation system BibRef

Kim, C.Y.[Chang-Yon], Gwak, J.[Jeonghwan], Shim, D.[Daeyoung], Jeon, M.[Moongu],
A framework for automatically constructing a dataset for training a vehicle detector,
IJCVR(9), No. 2, 2019, pp. 192-206.
DOI Link 1904
BibRef

Huang, Y.[Yu], Zhou, Z.H.[Zhi-Heng], Wang, T.L.[Tian-Lei], Cao, Q.[QiAn], Huang, J.C.[Jun-Chu], Chen, Z.R.[Zi-Rong],
A Part-Based Gaussian Reweighted Approach for Occluded Vehicle Detection,
IEICE(E102-D), No. 5, May 2019, pp. 1097-1101.
WWW Link. 1906
BibRef

Wang, Y.[Ye], Deng, W.W.[Wei-Wen], Liu, Z.Y.[Zhen-Yi], Wang, J.S.[Jin-Song],
Deep learning-based vehicle detection with synthetic image data,
IET-ITS(13), No. 7, July 2019, pp. 1097-1105.
DOI Link 1906
BibRef

Tong, G.F.[Guo-Feng], Chen, H.R.[Huai-Rong], Li, Y.[Yong], Du, X.[Xiance], Zhang, Q.C.[Qing-Chun],
Object detection for panoramic images based on MS-RPN structure in traffic road scenes,
IET-CV(13), No. 5, August 2019, pp. 500-506.
DOI Link 1908
BibRef

Bibars, A.[Ahmed], Mahroos, M.[Mohsen],
New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation,
IET-CV(13), No. 5, August 2019, pp. 443-451.
DOI Link 1908
BibRef

Liu, W., Liao, S., Hu, W.,
Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveillance Videos,
CirSysVideo(29), No. 12, December 2019, pp. 3558-3567.
IEEE DOI 1912
Videos, Feature extraction, Object detection, Detectors, Surveillance, Proposals, Dynamics, Object detection, deep neural network BibRef

Husain, A.A.[Agha Asim], Maity, T.[Tanmoy], Yadav, R.K.[Ravindra Kumar],
Vehicle detection in intelligent transport system under a hazy environment: a survey,
IET-IPR(14), No. 1, January 2020, pp. 1-10.
DOI Link 1912
BibRef

Teng, S.Z.[Shang-Zhi], Zhang, S.L.[Shi-Liang], Huang, Q.M.[Qing-Ming], Sebe, N.[Nicu],
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification,
IJCV(129), No. 3, March 2021, pp. 719-735.
Springer DOI 2103
BibRef

Xu, K.[Ke], Gong, H.[Hua], Liu, F.[Fang],
Vehicle detection based on improved multitask cascaded convolutional neural network and mixed image enhancement,
IET-IPR(14), No. 17, 24 December 2020, pp. 4621-4632.
DOI Link 2104
BibRef

Boukerche, A.[Azzedine], Hou, Z.J.[Zhi-Jun],
Object Detection Using Deep Learning Methods in Traffic Scenarios,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link 2104
convolutional neural networks, autonomous driving system, Object detection, vehicle detection, deep learning BibRef

Li, D.L.[Dong Lin], Prasad, M.[Mukesh], Liu, C.L.[Chih-Ling], Lin, C.T.[Chin-Teng],
Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning,
ITS(22), No. 5, May 2021, pp. 3146-3157.
IEEE DOI 2105
Vehicle detection, Image color analysis, Roads, Transforms, Feature extraction, Robustness, Deformable models, color transformation BibRef

Zhao, M.[Min], Zhong, Y.[Yuan], Sun, D.[Dihua], Chen, Y.H.[Yu-Hao],
Accurate and efficient vehicle detection framework based on SSD algorithm,
IET-IPR(15), No. 13, 2021, pp. 3094-3104.
DOI Link 2110
BibRef

Chen, G.[Guang], Wang, F.[Fa], Qu, S.Q.[San-Qing], Chen, K.[Kai], Yu, J.W.[Jun-Wei], Liu, X.Y.[Xiang-Yong], Xiong, L.[Lu], Knoll, A.[Alois],
Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods,
ITS(22), No. 12, December 2021, pp. 7699-7711.
IEEE DOI 2112
Laser radar, Robot sensing systems, intelligent transportation system BibRef

Zhang, J.Y.[Jin-Yu], Zhang, Y.F.[Yi-Fan],
Vehicular Localization Based on CSI-Fingerprint and Vector Match,
ITS(22), No. 12, December 2021, pp. 7736-7746.
IEEE DOI 2112
OFDM, Roads, Global Positioning System, Signal processing algorithms, filter BibRef

Luo, X.Y.[Xiao-Yue], Wang, Y.H.[Yan-Hui], Cai, B.[Benhe], Li, Z.X.[Zhan-Xing],
Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Tian, Y.[Yan], Chen, T.[Tao], Cheng, G.H.[Guo-Hua], Yu, S.H.[Shi-Hao], Li, X.[Xi], Li, J.Y.[Jian-Yuan], Yang, B.[Bailin],
Global Context Assisted Structure-Aware Vehicle Retrieval,
ITS(23), No. 1, January 2022, pp. 165-174.
IEEE DOI 2201
Semantics, Convolution, Image retrieval, Vehicle detection, Space vehicles, Image retrieval, deep learning, landmark alignment, intelligent transportation system BibRef

Shen, B.[Bo], Zhang, R.[Rui], Chen, H.[Hao],
An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Zheng, M.X.[Ming-Xue], Wu, H.Y.[Hua-Yi],
Vehicle Recognition Based on Region Growth of Relative Tension and Similarity Measurement of Side Projection Profile of Vehicle Body,
RS(15), No. 6, 2023, pp. 1493.
DOI Link 2304
BibRef

Oh, D.[Dahyun], Kang, K.[Kyubyung], Seo, S.[Sungchul], Xiao, J.[Jinwu], Jang, K.[Kyochul], Kim, K.[Kibum], Park, H.[Hyungkeun], Won, J.[Jeonghun],
Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Roy, D.[Debashri], Li, Y.Y.[Yuan-Yuan], Jian, T.[Tong], Tian, P.[Peng], Chowdhury, K.[Kaushik], Ioannidis, S.[Stratis],
Multi-Modality Sensing and Data Fusion for Multi-Vehicle Detection,
MultMed(25), 2023, pp. 2280-2295.
IEEE DOI 2306
Radar tracking, Sensors, Radar imaging, Radar, Acoustics, Visualization, Sensor fusion, Vehicle detection, tracking, radar BibRef

Lu, Y.F.[Yan-Feng], Gao, J.W.[Jing-Wen], Yu, Q.[Qian], Li, Y.[Yi], Lv, Y.S.[Yi-Sheng], Qiao, H.[Hong],
A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios,
ITS(24), No. 7, July 2023, pp. 6989-6999.
IEEE DOI 2307
Feature extraction, Object detection, Lighting, Traffic control, Adaptation models, Task analysis, Robustness, Traffic detection, illumination invariance BibRef

Luo, T.[Tong], Wang, H.[Hai], Cai, Y.F.[Ying-Feng], Chen, L.[Long], Wang, K.[Kuan], Yu, Y.J.[Yi-Jie],
Binary residual feature pyramid network: An improved feature fusion module based on double-channel residual pyramid structure for autonomous detection algorithm,
IET-ITS(17), No. 7, 2023, pp. 1288-1301.
DOI Link 2307
BibRef

Chen, C.[Chen], Wang, C.Y.[Chen-Yu], Liu, B.[Bin], He, C.[Ci], Cong, L.[Li], Wan, S.H.[Shao-Hua],
Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles,
ITS(24), No. 11, November 2023, pp. 13023-13034.
IEEE DOI 2311
BibRef

Ngo, B.H.[Bach Hoang], Nguyen, D.T.[Dat Thanh], Do-Tran, N.T.[Nhat-Tuong], Thien, P.P.H.[Phuc Pham Huy], An, M.H.[Minh-Hung], Nguyen, T.N.[Tuan-Ngoc], Hoang, L.N.[Loi Nguyen], Nguyen, V.D.[Vinh Dinh], Dinh, Q.V.[Quang-Vinh],
Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval,
AICity23(5409-5418)
IEEE DOI 2309
BibRef

Xie, D.[Dong], Liu, L.[Linhu], Zhang, S.J.[Sheng-Jun], Tian, J.[Jiang],
A Unified Multi-modal Structure for Retrieving Tracked Vehicles through Natural Language Descriptions,
AICity23(5419-5427)
IEEE DOI 2309
BibRef

Mokayed, H.[Hamam], Nayebiastaneh, A.[Amirhossein], De, K.[Kanjar], Sozos, S.[Stergios], Hagner, O.[Olle], Backe, B.[Björn],
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions,
AICity23(5314-5322)
IEEE DOI 2309
BibRef

García-Aguilar, I.[Iván], García-González, J.[Jorge], Luque-Baena, R.M.[Rafael Marcos], López-Rubio, E.[Ezequiel],
Automated labeling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks,
PRL(167), 2023, pp. 45-52.
Elsevier DOI 2303
Object detection, Small scale, Super-resolution, Convolutional neural networks BibRef

Seo, T.[Taewon], Park, K.H.[Kyung Ho], Chung, H.[Hyunhee],
SOCAR: Socially-Obtained CAR Dataset for Image Recognition in the Wild,
Novelty23(430-438)
IEEE DOI 2302
Image recognition, Computational modeling, Surveillance, Conferences, Reproducibility of results, Automobiles BibRef

Yuan, M.X.[Mao-Xun], Wang, Y.Y.[Yin-Yan], Wei, X.X.[Xing-Xing],
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection,
ECCV22(IX:509-525).
Springer DOI 2211
BibRef

Wang, Z.T.[Zhen-Ting], Li, W.[Wei], Wu, X.[Xiao], Sheng, L.[Luhan],
Learning Selective Assignment Network for Scene-Aware Vehicle Detection,
ICIP22(1366-1370)
IEEE DOI 2211
Training, Deep learning, Vehicle detection, Scalability, Semantics, Detectors, Object detection, Deep learning, object detection, vehicle detection BibRef

Zhang, J.C.[Jia-Cheng], Lin, X.R.[Xiang-Ru], Jiang, M.[Minyue], Yu, Y.[Yue], Gong, C.T.[Chen-Ting], Zhang, W.[Wei], Tan, X.[Xiao], Li, Y.Y.[Ying-Ying], Ding, E.[Errui], Li, G.B.[Guan-Bin],
A Multi-granularity Retrieval System for Natural Language-based Vehicle Retrieval,
AICity22(3215-3224)
IEEE DOI 2210
Training, Target tracking, Fuses, Urban areas, Semantics, Force, Linguistics BibRef

Miao, H.[Hui], Lu, F.X.[Fei-Xiang], Liu, Z.D.[Zong-Dai], Zhang, L.J.[Liang-Jun], Manocha, D.[Dinesh], Zhou, B.[Bin],
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS,
ICCV21(15611-15620)
IEEE DOI 2203
Training, Deep learning, Image segmentation, Annotations, Pose estimation, Vision for robotics and autonomous vehicles, Vision applications and systems BibRef

Liu, M.Y.[Meng-Yun], Qi, N.[Na], Shi, Y.H.[Yun-Hui], Yin, B.C.[Bao-Cai],
An Attention Fusion Network for Event-Based Vehicle Object Detection,
ICIP21(3363-3367)
IEEE DOI 2201
Location awareness, Uncertainty, Fuses, Object detection, Predictive models, Feature extraction, vehicle detection, Attention module BibRef

Zhang, H.T.[Hao-Tian], Ji, H.R.[Hao-Rui], Zheng, A.[Aotian], Hwang, J.N.[Jenq-Neng], Hwang, R.H.[Ren-Hung],
Monocular 3D Localization of Vehicles in Road Scenes,
AVVision21(2855-2864)
IEEE DOI 2112
Location awareness, Roads, Cameras, Iron, Sensors, Task analysis BibRef

Sun, P.[Pei], Wang, W.[Weiyue], Chai, Y.N.[Yu-Ning], Elsayed, G.[Gamaleldin], Bewley, A.[Alex], Zhang, X.[Xiao], Sminchisescu, C.[Cristian], Anguelov, D.[Dragomir],
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection,
CVPR21(5721-5730)
IEEE DOI 2111
Measurement, Laser radar, Head, Vehicle detection, Object detection, Detectors BibRef

Koestler, L.[Lukas], Yang, N.[Nan], Wang, R.[Rui], Cremers, D.[Daniel],
Learning Monocular 3D Vehicle Detection Without 3D Bounding Box Labels,
GCPR20(116-129).
Springer DOI 2110
BibRef

Hofstetter, I.[Isabell], Springer, M.[Malte], Ries, F.[Florian], Haueis, M.[Martin],
Constellation Codebooks for Reliable Vehicle Localization,
GCPR20(303-315).
Springer DOI 2110
BibRef

Bai, S.[Shuai], Zheng, Z.D.[Zhe-Dong], Wang, X.H.[Xiao-Han], Lin, J.Y.[Jun-Yang], Zhang, Z.[Zhu], Zhou, C.[Chang], Yang, H.X.[Hong-Xia], Yang, Y.[Yi],
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval,
AICity21(4029-4038)
IEEE DOI 2109
Code, Vehicle Detection.
WWW Link. Training, Smart cities, Search problems, Robustness, Pattern recognition, Task analysis BibRef

Lee, S.[Sangrok], Woo, T.[Taekang], Lee, S.H.[Sang Hun],
SBNet: Segmentation-based Network for Natural Language-based Vehicle Search,
AICity21(4049-4055)
IEEE DOI 2109
Deep learning, Image segmentation, Law enforcement, Urban areas, Natural languages BibRef

Sun, Z.[Ziruo], Liu, X.F.[Xin-Fang], Bi, X.P.[Xiao-Peng], Nie, X.S.[Xiu-Shan], Yin, Y.L.[Yi-Long],
DUN: Dual-path Temporal Matching Network for Natural Language-based Vehicle Retrieval,
AICity21(4056-4062)
IEEE DOI 2109
Visualization, Silver, Databases, Natural languages, Urban areas, Feature extraction BibRef

Khorramshahi, P.[Pirazh], Rambhatla, S.S.[Sai Saketh], Chellappa, R.[Rama],
Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems,
AICity21(4178-4187)
IEEE DOI 2109
Training, Visualization, Adaptation models, Urban areas, Cameras BibRef

Park, E.J.[Eun-Ju], Kim, H.[Hoyoung], Jeong, S.[Seonghwan], Kang, B.[Byungkon], Kwon, Y.[YoungMin],
Keyword-based Vehicle Retrieval,
AICity21(4215-4222)
IEEE DOI 2109
Training, Deep learning, Urban areas, Semantics, Streaming media, Feature extraction, Particle measurements BibRef

Nguyen, T.M.[Tam Minh], Pham, Q.H.[Quang Huu], Doan, L.B.[Linh Bao], Trinh, H.V.[Hoang Viet], Nguyen, V.A.[Viet-Anh], Phan, V.H.[Viet-Hoang],
Contrastive Learning for Natural Language-Based Vehicle Retrieval,
AICity21(4240-4247)
IEEE DOI 2109
Target tracking, Urban areas, Natural languages, Pattern recognition BibRef

Scribano, C.[Carmelo], Sapienza, D.[Davide], Franchini, G.[Giorgia], Verucchi, M.[Micaela], Bertogna, M.[Marko],
All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers,
AICity21(4248-4257)
IEEE DOI 2109
Training, Visualization, Target tracking, Natural languages, Urban areas, Loss measurement BibRef

Lee, C.[Chaehyeon], Seo, J.[Junghoon], Jung, H.[Heechul],
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner,
EarthVision21(1172-1181)
IEEE DOI 2109
Training, Vehicle detection, Object detection, Detectors, Benchmark testing BibRef

Azimi, S.M.[Seyed Majid], Bahmanyar, R.[Reza], Henry, C.[Corentin], Kurz, F.[Franz],
EAGLE: Large-Scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery,
ICPR21(6920-6927)
IEEE DOI 2105
Photography, Vehicle detection, Urban areas, Superresolution, Object detection, Tools, Cameras BibRef

Wang, X., Hu, X., Chen, C., Fan, Z., Peng, S.,
Illuminating Vehicles With Motion Priors For Surveillance Vehicle Detection,
ICIP20(2021-2025)
IEEE DOI 2011
Detectors, Surveillance, Feature extraction, Videos, Training, Object detection, Roads, Motion priors, vehicle detection, traffic surveillance videos BibRef

Woo, S., Hwang, S., Kim, W., Lee, J., Lee, D., Lee, S.,
False Positive Removal for 3D Vehicle Detection With Penetrated Point Classifier,
ICIP20(2721-2725)
IEEE DOI 2011
Laser radar, Solid modeling, Automobiles, Shape, Autonomous vehicles, autonomous driving BibRef

Yao, Y.[Yue], Zheng, L.[Liang], Yang, X.D.[Xiao-Dong], Naphade, M.[Milind], Gedeon, T.[Tom],
Simulating Content Consistent Vehicle Datasets with Attribute Descent,
ECCV20(VI:775-791).
Springer DOI 2011
Code, Vehicle Synthesis.
WWW Link. BibRef

Peri, N., Khorramshahi, P., Rambhatla, S.S., Shenoy, V., Rawat, S., Chen, J., Chellappa, R.,
Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection,
City20(2648-2657)
IEEE DOI 2008
Cameras, Task analysis, Training, Feature extraction, Anomaly detection, Robustness, Computational modeling BibRef

Qian, Y.J.[Yi-Jun], Yu, L.J.[Li-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City,
City20(2511-2519)
IEEE DOI 2008
Cameras, Feature extraction, Urban areas, Target tracking, Task analysis, Object detection, Computational modeling BibRef

Yu, L.J.[Li-Jun], Qian, Y.J.[Yi-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals,
Activity22(112-121)
IEEE DOI 2202
Surveillance, Roads, Streaming media, NIST, Benchmark testing, Real-time systems BibRef

Yu, L.J.[Li-Jun], Feng, Q.Y.[Qian-Yu], Qian, Y.J.[Yi-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation,
City20(2534-2543)
IEEE DOI 2008
Cameras, Target tracking, Trajectory, Videos, Automobiles, Task analysis BibRef

Liu, Z., Lian, T., Farrell, J., Wandell, B.,
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network,
ADW19(2383-2392)
IEEE DOI 2004
cameras, convolutional neural nets, image processing, object detection, photography, software simulations, metrics BibRef

Yan, X., Yu, Y., Wang, F., Liu, W., He, S., Pan, J.,
Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery,
ICCV19(7617-7626)
IEEE DOI 2004
hidden feature removal, image segmentation, object tracking, traffic engineering computing, video signal processing, Training BibRef

Fattal, A.K.[Ann-Katrin], Karg, M.[Michelle], Scharfenberger, C.[Christian], Adamy, J.[Jürgen],
Distant Vehicle Detection: How Well Can Region Proposal Networks Cope with Tiny Objects at Low Resolution?,
CVRoads18(I:289-304).
Springer DOI 1905
BibRef

Gupta, I.[Ishan], Rangesh, A.[Akshay], Trivedi, M.[Mohan],
3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach Using Single Monocular Images,
AutoNUE18(V:626-641).
Springer DOI 1905
BibRef

Sandhu, M.[Mahtab], Upadhyay, S.[Sarthak], Krishna, M.[Madhava], Medasani, S.[Shanti],
Motion Segmentation Using Spectral Clustering on Indian Road Scenes,
AutoNUE18(V:676-687).
Springer DOI 1905
BibRef

Sun, Y., Li, M., Lu, J.,
Part-based Multi-stream Model for Vehicle Searching,
ICPR18(1372-1377)
IEEE DOI 1812
Streaming media, Training, Task analysis, Measurement, Feature extraction, Computational modeling, Multi-stream CNN BibRef

Sheeny, M., Wallace, A., Emambakhsh, M., Wang, S., Connor, B.,
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors,
PBVS18(1328-13286)
IEEE DOI 1812
Convolution, Feature extraction, Neural networks, Meteorology, Sensors, Object detection, Training BibRef

Tokuda, E.K., Ferreira, G.B.A., Silva, C., Cesar, R.M.,
A Novel Semi-Supervised Detection Approach with Weak Annotation,
Southwest18(129-132)
IEEE DOI 1809
Detectors, Quality control, Training, Automobiles, Cameras, Meteorology, Videos BibRef

Fan, W., Ainouz, S., Meriaudeau, F., Bensrhair, A.,
Polarization-Based Car Detection,
ICIP18(3069-3073)
IEEE DOI 1809
Feature extraction, Automobiles, Image color analysis, Color, Roads, Computational modeling, Detectors, Car detection, polarization, road scenes BibRef

Wang, X., Cheng, P., Liu, X., Uzochukwu, B.,
Focal loss dense detector for vehicle surveillance,
ISCV18(1-5)
IEEE DOI 1807
convolution, feedforward neural nets, learning (artificial intelligence), object detection, Vehicle detection BibRef

Moate, C.P.[Chris P.], Hayward, S.D.[Stephen D.], Ellis, J.S.[Jonathan S.], Russell, L.[Lee], Timmerman, R.O.[Ralph O.], Lane, R.O.[Richard O.], Strain, T.J.[Thomas J.],
Vehicle Detection in Infrared Imagery Using Neural Networks with Synthetic Training Data,
ICIAR18(453-461).
Springer DOI 1807
BibRef

Yang, B.[Biao], Zhang, Y.Y.[Yu-Yu], Cao, J.M.[Jin-Meng], Zou, L.[Ling],
On Road Vehicle Detection Using an Improved Faster RCNN Framework with Small-Size Region Up-Scaling Strategy,
PSIVTWS17(241-253).
Springer DOI 1806
BibRef

Rujikietgumjorn, S., Watcharapinchai, N.,
Vehicle detection with sub-class training using R-CNN for the UA-DETRAC benchmark,
AVSS17(1-5)
IEEE DOI 1806
feature extraction, learning (artificial intelligence), object detection, road vehicles, traffic engineering computing, Vehicle detection BibRef

Yuan, X.[Xue], Su, S.A.[Shu-Ai], Chen, H.J.[Hou-Jin],
A Graph-Based Vehicle Proposal Location and Detection Algorithm,
ITS(18), No. 12, December 2017, pp. 3282-3289.
IEEE DOI 1712
Cameras, Image color analysis, Image segmentation, Proposals, Sensors, Shape, Vehicle detection, graph based, vehicle proposal location BibRef

Espinosa, J.E.[Jorge E.], Velastin, S.A.[Sergio A.], Branch, J.W.[John W.],
Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study,
IVIC17(3-15).
Springer DOI 1711
BibRef

Castrejón, L.[Lluís], Kundu, K.[Kaustav], Urtasun, R.[Raquel], Fidler, S.[Sanja],
Annotating Object Instances with a Polygon-RNN,
CVPR17(4485-4493)
IEEE DOI 1711
Award, CVPR, HM. Agriculture, Image segmentation, Kernel, Labeling. Interactive segmentation of objects. Annotation. Cityscapes, Cars. BibRef

Lee, J.T., Chung, Y.,
Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks,
Traffic17(920-925)
IEEE DOI 1709
Automobiles, Bicycles, Error analysis, Lighting, Training, Training, data BibRef

Liu, J.X.[Ji-Xin], Sun, N.[Ning], Han, G.[Guang], Yang, H.G.[Hai-Gen],
Vehicle sparse recognition via class dictionary learning,
ICIVC17(185-188)
IEEE DOI 1708
Automobiles, Bicycles, Databases, Dictionaries, Motorcycles, Training, dictionary learning, intelligent transportation, sparse framework, vehicle recognition BibRef

Farhat, M., Mhiri, S., Tagina, M.,
Free training object detection based on multi-stage fusion using belief functions,
ISIVC16(153-158)
IEEE DOI 1704
Automobiles BibRef

van den Brand, J.[Jan], Ochs, M.[Matthias], Mester, R.[Rudolf],
Instance-Level Segmentation of Vehicles by Deep Contours,
CVTSV16(I: 477-492).
Springer DOI 1704
BibRef

Møgelmose, A., Moeslund, T.B.,
Analyzing Wheels of Vehicles in Motion Using Laser Scanning,
Traffic16(1601-1608)
IEEE DOI 1612
BibRef

Czapla, Z.[Zbigniew],
Orthogonal Gradient-Based Binary Image Representation for Vehicle Detection,
ICCVG16(453-461).
Springer DOI 1611
BibRef

Zhou, Y.[Yi], Liu, L.[Li], Shao, L.[Ling], Mellor, M.[Matt],
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation,
ECCV16(II: 278-293).
Springer DOI 1611
BibRef

Fang, Y., Sun, L., Fu, H., Wu, T., Wang, R., Dai, B.,
Learning deep compact channel features for object detection in traffic scenes,
ICIP16(1052-1056)
IEEE DOI 1610
Benchmark testing BibRef

Huang, J., You, S.,
Vehicle detection in urban point clouds with orthogonal-view convolutional neural network,
ICIP16(2593-2597)
IEEE DOI 1610
Automobiles BibRef

Xu, H., Huang, Q., Kuo, C.C.J.,
Car detection using deformable part models with composite features,
ICIP16(3812-3816)
IEEE DOI 1610
Automobiles BibRef

Sedaghat, N.[Nima], Brox, T.[Thomas],
Unsupervised Generation of a View Point Annotated Car Dataset from Videos,
ICCV15(1314-1322)
IEEE DOI 1602
Cameras. Generation of the dataset. BibRef

Struwe, M.[Marvin], Hasler, S.[Stephan], Bauer-Wersing, U.[Ute],
Rendered Benchmark Data Set for Evaluation of Occlusion-Handling Strategies of a Parts-Based Car Detector,
PSIVT15(99-110).
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Dataset, Vehicle Detection. BibRef

Shahraki, F.F.[Farideh Foroozandeh], Yazdanpanah, A.P.[Ali Pour], Regentova, E.E.[Emma E.], Muthukumar, V.[Venkatesan],
Bicycle Detection Using HOG, HSC and MLBP,
ISVC15(II: 554-562).
Springer DOI 1601
BibRef

Dekkiche, D.[Djamila], Vincke, B.[Bastien], Mérigot, A.[Alain],
Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification,
ISVC15(I: 762-773).
Springer DOI 1601
BibRef

Bloisi, D.D., Iocchi, L., Pennisi, A., Tombolini, L.,
ARGOS: Venice Boat Classification,
AVSS15(1-6)
IEEE DOI 1511
boats BibRef

Shih, H.C.[Huang-Chia], Wang, H.Y.[Hao-You],
Vehicle identification using distance-based appearance model,
AVSS15(1-4)
IEEE DOI 1511
Calibration BibRef

Yang, L.J.[Lin-Jie], Luo, P.[Ping], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
A large-scale car dataset for fine-grained categorization and verification,
CVPR15(3973-3981)
IEEE DOI 1510
BibRef

Naiel, M.A.[Mohamed A.], Ahmad, M.O.[M. Omair], Swamy, M.N.S.,
Vehicle Detection Using Approximation of Feature Pyramids in the DFT Domain,
ICIAR15(429-436).
Springer DOI 1507
BibRef

Takeuchi, R., Kato, K., Harwood, D., Davis, L.S.,
Vehicle detection using PLS Hough transform,
FCV15(1-6)
IEEE DOI 1506
Hough transforms BibRef

Kurnianggoro, L., Wahyono, Jo, K.H.[Kang-Hyun],
Utilization of optimally selected features for car detection in calibrated camera and LRF system,
FCV15(1-4)
IEEE DOI 1506
feature extraction BibRef

Wong, D.[David], Deguchi, D.[Daisuke], Ide, I.[Ichiro], Murase, H.[Hiroshi],
Vision-Based Vehicle Localization Using a Visual Street Map with Embedded SURF Scale,
CVVT14(167-179).
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Wei, L.J.[Li-Jun], Soheilian, B.[Bahman], Gouet-Brunet, V.[Valérie],
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CVVT14(194-208).
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Zhang, D.H.[Dong-Hao], He, X.M.[Xu-Ming], Li, H.X.[Han-Xi],
Data-Driven Street Scene Layout Estimation for Distant Object Detection,
DICTA14(1-7)
IEEE DOI 1502
image matching. Find similar layouts to images. BibRef

Ballesteros, G.[Gonzalo], Salgado, L.[Luis],
Histograms of oriented gradients for fast on-board vehicle verification,
ICIP14(1638-1642)
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Multi-scale car detection and localization using contour fragments,
ICIP14(1609-1613)
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Active contours BibRef

Elmikaty, M.[Mohamed], Stathaki, T.[Tania],
Car Detection in High-Resolution Urban Scenes Using Multiple Image Descriptors,
ICPR14(4299-4304)
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Feris, R.[Rogerio], Brown, L.M.[Lisa M.], Pankanti, S.[Sharath], Sun, M.T.[Ming-Ting],
Appearance-Based Object Detection Under Varying Environmental Conditions,
ICPR14(166-171)
IEEE DOI 1412
Cameras; Detectors; Lighting; Standards; Surveillance; Training; Vehicles BibRef

Jaccard, N.[Nicolas], Rogers, T.W.[Thomas W.], Griffin, L.D.[Lewis D.],
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CVPR15(1788-1797)
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Duan, K.[Kun], Marchesotti, L.[Luca], Crandall, D.J.[David J.],
Attribute-based vehicle recognition using viewpoint-aware multiple instance SVMs,
WACV14(333-338)
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Li, B.[Bo], Hu, W.Z.[Wen-Ze], Wu, T.F.[Tian-Fu], Zhu, S.C.[Song-Chun],
Modeling Occlusion by Discriminative AND-OR Structures,
ICCV13(2560-2567)
IEEE DOI 1403
AND-OR structure; CAD simulation; Car Detection; Occlusion Modeling BibRef

Rezaei, M.[Mahdi], Terauchi, M.[Mutsuhiro],
Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory,
PSIVT13(60-72).
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Chen, Y.C.[Yu-Chun], Su, T.F.[Te-Feng], Lai, S.H.[Shang-Hong],
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ICIP13(3321-3325)
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Vehicle detection BibRef

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Vehicle dimensions estimation scheme using AAM on stereoscopic video,
AVSS13(478-482)
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Bus Detection for Intelligent Transport Systems Using Computer Vision,
CIARP13(II:59-66).
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Audio-Visual Feature Fusion for Vehicles Classification in a Surveillance System,
PBVS13(381-386)
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Wang, T.[Tao], Zhu, Z.G.[Zhi-Gang],
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Han, D.J.[Dong-Jin], Hwang, J.[Jae], Hahn, H.S.[Hern-Soo], Cooper, D.B.[David B.],
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Jang, D.M.[Daniel Marcus], Turk, M.A.[Matthew A.],
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Godec, M., Leistner, C., Bischof, H., Starzacher, A., Rinner, B.,
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AVSS10(586-592).
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Tsai, Y.M.[Yi-Min], Huang, K.Y.[Keng-Yen], Tsai, C.C.[Chih-Chung], Chen, L.G.[Liang-Gee],
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Vision-Based Detection of Mobile Smart Objects,
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Mobile object recognition using multi-sensor information fusion in urban environments,
ICIP08(2384-2387).
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Klausner, A., Tengg, A., Rinner, B.,
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Vehicle Detection Based on Color and Edge Information,
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Vehicle Detection Using Double Slit Camera,
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Boosted Gabor Features Applied to Vehicle Detection,
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Gronwall, C., Anderson, P., Gustafsson, F.[Fredrik],
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SafeSecur05(III: 116-116).
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Vehicle detection fusing 2D visual features,
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Cucchiara, R., Piccardi, M., Prati, A., Scarabottolo, N.,
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Nighttime Vehicle Detection and Recognition .

Last update:Nov 30, 2023 at 15:51:27