Stanford Cars Dataset,
2019.
A dataset for understanding human actions in still images
WWW Link.
HTML Version.
Dataset, Vehicles. 196 classes of cars, 16,185 images.
See also Leveraging the Wisdom of the Crowd for Fine-Grained Recognition.
See also Stanford University, Computer Science Departent.
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Anderson, J.A.D.W.,
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BibRef
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Vision-based bicycle/motorcycle classification,
PRL(28), No. 13, 1 October 2007, pp. 1719-1726.
Elsevier DOI
0709
Traffic monitoring; Feature extraction; Support Vector Machine;
Vehicle classification; Image analysis
BibRef
Ghosh, N.[Nirmalya],
Bhanu, B.[Bir],
Incremental Unsupervised Three-Dimensional Vehicle Model Learning From
Video,
ITS(11), No. 2, June 2010, pp. 423-440.
IEEE DOI
1007
BibRef
Earlier:
How current BNs fail to represent evolvable pattern recognition
problems and a proposed solution,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Earlier:
Bayesian based 3D shape reconstruction from video,
ICIP08(1152-1155).
IEEE DOI
0810
BibRef
Earlier:
Incremental Vehicle 3-D Modeling from Video,
ICPR06(III: 272-275).
IEEE DOI
0609
BibRef
Zhang, B.,
Reliable Classification of Vehicle Types Based on Cascade Classifier
Ensembles,
ITS(14), No. 1, March 2013, pp. 322-332.
IEEE DOI
1303
BibRef
Ghosh, N.[Nirmalya],
Bhanu, B.[Bir],
Evolving Bayesian Graph for Three-Dimensional Vehicle Model Building
From Video,
ITS(15), No. 2, April 2014, pp. 563-578.
IEEE DOI
1404
Buildings
BibRef
Thakoor, N.S.[Ninad S.],
Bhanu, B.[Bir],
Structural Signatures for Passenger Vehicle Classification in Video,
ITS(14), No. 4, 2013, pp. 1796-1805.
IEEE DOI
1312
BibRef
Earlier:
ICPR12(926-929).
WWW Link.
1302
Feature extraction
BibRef
Thakoor, N.S.[Ninad S.],
Bhanu, B.[Bir],
Efficient alignment for vehicle make and model recognition,
ICIP14(5542-5546)
IEEE DOI
1502
Accuracy
BibRef
Zhang, B.L.[Bai-Ling],
Zhao, C.H.[Chi-Hang],
He, J.[Jie],
Classification of vehicle type and make by combined features and random
subspace ensemble,
IJCVR(3), No. 1-2, 2012, pp. 35-51.
DOI Link
1204
BibRef
Earlier: A1, A2, Only:
Classification of Vehicle Make by Combined Features and Random Subspace
Ensemble,
ICIG11(920-925).
IEEE DOI
1109
BibRef
Zhang, B.L.[Bai-Ling],
Classification and identification of vehicle type and make by
cortex-like image descriptor HMAX,
IJCVR(4), No. 3, 2014, pp. 195-211.
DOI Link
1407
BibRef
Zhang, B.L.[Bai-Ling],
Zhou, Y.F.[Yi-Fan],
Pan, H.[Hao],
Tillo, T.[Tammam],
Hybrid model of clustering and kernel autoassociator for reliable
vehicle type classification,
MVA(25), No. 2, February 2014, pp. 437-450.
WWW Link.
1402
BibRef
Chen, Z.Z.[Ze-Zhi],
Pears, N.,
Freeman, M.,
Austin, J.,
A Gaussian mixture model and support vector machine approach to
vehicle type and colour classification,
IET-ITS(8), No. 2, March 2014, pp. 135-144.
DOI Link
1406
Gaussian processes
BibRef
Hsieh, J.W.[Jun-Wei],
Chen, L.C.[Li-Chih],
Chen, D.Y.[Duan-Yu],
Symmetrical SURF and Its Applications to Vehicle Detection and
Vehicle Make and Model Recognition,
ITS(15), No. 1, February 2014, pp. 6-20.
IEEE DOI
1403
feature extraction
BibRef
Chen, Z.Z.[Ze-Zhi],
Ellis, T.,
Semi-automatic annotation samples for vehicle type classification in
urban environments,
IET-ITS(9), No. 3, 2015, pp. 240-249.
DOI Link
1506
closed circuit television
BibRef
Dong, Z.[Zhen],
Wu, Y.,
Pei, M.T.[Ming-Tao],
Jia, Y.D.[Yun-De],
Vehicle Type Classification Using a Semisupervised Convolutional
Neural Network,
ITS(16), No. 4, August 2015, pp. 2247-2256.
IEEE DOI
1508
Convolution
BibRef
Dong, Z.[Zhen],
Pei, M.T.[Ming-Tao],
He, Y.[Yang],
Liu, T.[Ting],
Dong, Y.M.[Yan-Mei],
Jia, Y.D.[Yun-De],
Vehicle Type Classification Using Unsupervised Convolutional Neural
Network,
ICPR14(172-177)
IEEE DOI
1412
Accuracy
BibRef
Chen, L.C.[Li-Chih],
Hsieh, J.W.[Jun-Wei],
Yan, Y.L.[Yi-Lin],
Chen, D.Y.[Duan-Yu],
Vehicle make and model recognition using sparse representation and
symmetrical SURFs,
PR(48), No. 6, 2015, pp. 1979-1998.
Elsevier DOI
1503
Symmetrical SURF
BibRef
Hsieh, J.W.[Jun-Wei],
Chen, L.C.[Li-Chih],
Chen, D.Y.[Duan-Yu],
Cheng, S.C.[Shyi-Chyi],
Vehicle make and model recognition using symmetrical SURF,
AVSS13(472-477)
IEEE DOI
1311
computer vision
BibRef
He, H.,
Shao, Z.,
Tan, J.,
Recognition of Car Makes and Models From a Single Traffic-Camera
Image,
ITS(16), No. 6, December 2015, pp. 3182-3192.
IEEE DOI
1512
Cameras
BibRef
Siddiqui, A.J.[Abdul Jabbar],
Mammeri, A.[Abdelhamid],
Boukerche, A.[Azzedine],
Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF
Features,
ITS(17), No. 11, November 2016, pp. 3205-3219.
IEEE DOI
1609
Dictionaries
BibRef
Boukerche, A.[Azzedine],
Siddiqui, A.J.[Abdul Jabbar],
Mammeri, A.[Abdelhamid],
Automated Vehicle Detection and Classification: Models, Methods, and
Techniques,
Surveys(50), No. 5, November 2017, pp. Article No 62.
DOI Link
1712
Survey, Vehicle Detection. Categorize based on granularity of recognition.
BibRef
Bitar, N.,
Refai, H.H.,
A Probabilistic Approach to Improve the Accuracy of Axle-Based
Automatic Vehicle Classifiers,
ITS(18), No. 3, March 2017, pp. 537-544.
IEEE DOI
1703
Axles
BibRef
Fang, J.,
Zhou, Y.,
Yu, Y.,
Du, S.,
Fine-Grained Vehicle Model Recognition Using A Coarse-to-Fine
Convolutional Neural Network Architecture,
ITS(18), No. 7, July 2017, pp. 1782-1792.
IEEE DOI
1706
Feature extraction, Image recognition, Solid modeling,
Support vector machines, Training, Vehicles,
Fine-grained vehicle recognition, convolutional neural network,
one-versus-all, SVM
BibRef
Biglari, M.[Mohsen],
Soleimani, A.[Ali],
Hassanpour, H.[Hamid],
Part-based recognition of vehicle make and model,
IET-IPR(11), No. 7, July 2017, pp. 483-491.
DOI Link
1707
BibRef
Chen, L.[Long],
He, Y.H.[Yu-Hang],
Fan, L.[Lei],
Let the robot tell: Describe car image with natural language via LSTM,
PRL(98), No. 1, 2017, pp. 75-82.
Elsevier DOI
1710
BibRef
Hu, Q.,
Wang, H.,
Li, T.,
Shen, C.,
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car
Recognition,
ITS(18), No. 11, November 2017, pp. 3147-3156.
IEEE DOI
1711
Automobiles, Cameras, Feature extraction, Robustness, Surveillance,
car model classification.
BibRef
Biglari, M.,
Soleimani, A.,
Hassanpour, H.,
A Cascaded Part-Based System for Fine-Grained Vehicle Classification,
ITS(19), No. 1, January 2018, pp. 273-283.
IEEE DOI
1801
Automobiles, Data mining, Deformable models, Feature extraction,
Support vector machines, Training, Fine-grained classification,
vehicle make and model recognition
BibRef
Gao, Y.B.[Yong-Bin],
Lee, H.J.[Hyo Jong],
Car manufacturer and model recognition based on scale invariant feature
transform,
IJCVR(8), No. 1, 2018, pp. 32-41.
DOI Link
1804
BibRef
Yang, D.[Dan],
Qian, Y.L.[Yan-Lin],
Chen, K.[Ke],
Berki, E.[Eleni],
Kämäräinen, J.K.[Joni-Kristian],
Hierarchical Sliding Slice Regression for Vehicle Viewing Angle
Estimation,
ITS(19), No. 6, June 2018, pp. 2035-2042.
IEEE DOI
1806
Automobiles, Correlation, Estimation, Manifolds, Robustness,
Space vehicles, Visualization, Visual regression,
viewing angle estimation
BibRef
Luo, Z.,
Branchaud-Charron, F.,
Lemaire, C.,
Konrad, J.,
Li, S.,
Mishra, A.,
Achkar, A.,
Eichel, J.,
Jodoin, P.,
MIO-TCD: A New Benchmark Dataset for Vehicle Classification and
Localization,
IP(27), No. 10, October 2018, pp. 5129-5141.
IEEE DOI
1808
cameras, image classification,
learning (artificial intelligence), object detection,
vehicle classification
BibRef
Wang, J.T.[Ji-Tian],
Zheng, H.[Han],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Vehicle Type Recognition in Surveillance Images From Labeled
Web-Nature Data Using Deep Transfer Learning,
ITS(19), No. 9, September 2018, pp. 2913-2922.
IEEE DOI
1809
Surveillance, Image recognition, Machine learning, Training, Imaging,
Vehicle type recognition, surveillance images,
unsupervised domain adaptation
BibRef
Huang, Y.[Yue],
Liu, Z.W.[Zhen-Wei],
Jiang, M.H.[Ming-Hui],
Yu, X.[Xian],
Ding, X.H.[Xing-Hao],
Cost-Effective Vehicle Type Recognition in Surveillance Images With
Deep Active Learning and Web Data,
ITS(21), No. 1, January 2020, pp. 79-86.
IEEE DOI
2001
Surveillance, Image recognition, Entropy, Training,
Feature extraction, Deep learning, Uncertainty, Active learning,
vehicle type recognition
BibRef
Wei, J.[Jie],
Liu, C.H.[Chi-Him],
Clouse, H.[Hamilton],
Spectral Eigen Index: Military vehicle fingerprinting using Eigen
analysis in spectral domain,
PRL(112), 2018, pp. 98-103.
Elsevier DOI
1809
BibRef
Zaki, M.H.[Mohamed H.],
Sayed, T.[Tarek],
Automated class identification of modes of travel in shared spaces:
A case study from India,
IET-ITS(12), No. 8, October 2018, pp. 765-773.
DOI Link
1809
Motorized vs. non, cars, buses, etc.
BibRef
Rachmadi, R.F.[Reza Fuad],
Uchimura, K.[Keiichi],
Koutaki, G.[Gou],
Ogata, K.[Kohichi],
Single image vehicle classification using pseudo long short-term
memory classifier,
JVCIR(56), 2018, pp. 265-274.
Elsevier DOI
1811
Pseudo-LSTM classifier, Vehicle classification, Deep convolutional network
BibRef
Sochor, J.[Jakub],
Španhel, J.[Jakub],
Herout, A.[Adam],
BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D
Bounding Boxes in Traffic Surveillance,
ITS(20), No. 1, January 2019, pp. 97-108.
IEEE DOI
1901
Solid modeling, Feature extraction,
Surveillance, Cameras, Training, Task analysis,
convolutional neural network
BibRef
Sochor, J.[Jakub],
Herout, A.[Adam],
Havel, J.,
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle
Recognition,
CVPR16(3006-3015)
IEEE DOI
1612
BibRef
Wang, X.C.[Xin-Chen],
Zhang, W.W.[Wei-Wei],
Wu, X.C.[Xun-Cheng],
Xiao, L.Y.[Ling-Yun],
Qian, Y.B.[Yu-Bin],
Fang, Z.[Zhi],
Real-time vehicle type classification with deep convolutional neural
networks,
RealTimeIP(16), No. 1, February 2019, pp. 5-14.
Springer DOI
WWW Link.
1902
BibRef
Ghassemi, S.[Sina],
Fiandrotti, A.[Attilio],
Caimotti, E.[Emanuele],
Francini, G.[Gianluca],
Magli, E.[Enrico],
Vehicle joint make and model recognition with multiscale attention
windows,
SP:IC(72), 2019, pp. 69-79.
Elsevier DOI
1902
Vehicle classification, Convolutional neural network,
Attention windows, Residual network
BibRef
Soon, F.C.,
Khaw, H.Y.,
Chuah, J.H.,
Kanesan, J.,
PCANet-Based Convolutional Neural Network Architecture for a Vehicle
Model Recognition System,
ITS(20), No. 2, February 2019, pp. 749-759.
IEEE DOI
1902
Feature extraction, Principal component analysis,
Image segmentation, Licenses, Training, Robustness,
vehicle model recognition
BibRef
Hussain, K.F.,
Afifi, M.,
Moussa, G.,
A Comprehensive Study of the Effect of Spatial Resolution and Color
of Digital Images on Vehicle Classification,
ITS(20), No. 3, March 2019, pp. 1181-1190.
IEEE DOI
1903
Spatial resolution, Image color analysis, Feature extraction,
Visualization, Digital cameras, Digital images, Gray-scale,
deep learning
BibRef
Elkerdawy, S.[Sara],
Ray, N.[Nilanjan],
Zhang, H.[Hong],
Fine-Grained Vehicle Classification with Unsupervised Parts
Co-occurrence Learning,
WiCV-E18(IV:664-670).
Springer DOI
1905
BibRef
Lu, L.,
Huang, H.,
A Hierarchical Scheme for Vehicle Make and Model Recognition From
Frontal Images of Vehicles,
ITS(20), No. 5, May 2019, pp. 1774-1786.
IEEE DOI
1905
Feature extraction, Task analysis, Licenses, Solid modeling,
Image recognition, Intelligent transportation systems,
vehicle make and model recognition (VMMR)
BibRef
Sun, W.[Wei],
Zhang, X.R.[Xiao-Rui],
Shi, S.S.[Shun-Shun],
He, X.Z.[Xiao-Zheng],
Vehicle classification approach based on the combined texture and shape
features with a compressive DL,
IET-ITS(13), No. 7, July 2019, pp. 1069-1077.
DOI Link
1906
BibRef
Chen, Z.B.[Zhi-Bo],
Ying, C.L.[Chen-Lu],
Lin, C.Y.[Chao-Yi],
Liu, S.[Sen],
Li, W.P.[Wei-Ping],
Multi-View Vehicle Type Recognition With Feedback-Enhancement
Multi-Branch CNNs,
CirSysVideo(29), No. 9, September 2019, pp. 2590-2599.
IEEE DOI
1909
Video recording, Cameras, Visualization, Training,
Feature extraction, Kernel, Circuits and systems, VTR, multi-view, CNN
BibRef
Zhu, L.Z.[Ling-Zhi],
Zhang, S.N.[Shu-Ning],
Chen, S.[Si],
Zhao, H.C.[Hui-Chang],
Lu, X.Y.[Xiang-Yu],
Wei, D.X.[Dong-Xu],
Classification of UAV-to-ground vehicles based on micro-Doppler effect
and bispectrum analysis,
SIViP(14), No. 1, February 2020, pp. 19-27.
Springer DOI
2001
Distinguish ground wheeled vehicles from ground tracked vehicles.
BibRef
Shvai, N.,
Hasnat, A.,
Meicler, A.,
Nakib, A.,
Accurate Classification for Automatic Vehicle-Type Recognition Based
on Ensemble Classifiers,
ITS(21), No. 3, March 2020, pp. 1288-1297.
IEEE DOI
2003
Vehicle classification, convolutional neural network, gradient boosting
BibRef
Theagarajan, R.,
Thakoor, N.S.,
Bhanu, B.,
Physical Features and Deep Learning-based Appearance Features for
Vehicle Classification from Rear View Videos,
ITS(21), No. 3, March 2020, pp. 1096-1108.
IEEE DOI
2003
Visualization, Licenses, Feature extraction, Cameras, Videos,
Deep learning, Traffic control, Convolutional neural networks,
vehicle classification on highways/freeways
BibRef
Nazemi, A.,
Azimifar, Z.,
Shafiee, M.J.,
Wong, A.,
Real-Time Vehicle Make and Model Recognition Using Unsupervised
Feature Learning,
ITS(21), No. 7, July 2020, pp. 3080-3090.
IEEE DOI
2007
Feature extraction, Lighting, Real-time systems, Licenses,
Intelligent transportation systems, Meteorology,
fine-grained image classification
BibRef
Xiang, Y.,
Fu, Y.,
Huang, H.,
Global Topology Constraint Network for Fine-Grained Vehicle
Recognition,
ITS(21), No. 7, July 2020, pp. 2918-2929.
IEEE DOI
2007
Topology, Annotations, Convolution, Network topology,
Feature extraction, Automobiles, Kernel,
convolution neural network (CNN)
BibRef
Yu, Y.[Ye],
Xu, L.D.[Long-Dao],
Jia, W.[Wei],
Zhu, W.J.[Wen-Jia],
Fu, Y.X.[Yun-Xiang],
Lu, Q.A.[Qi-Ang],
CAM: A fine-grained vehicle model recognition method based on visual
attention model,
IVC(104), 2020, pp. 104027.
Elsevier DOI
2012
Vehicle model recognition, Visual attention mechanism,
Long short-term memory (LSTM) network, Convolutional LSTM
BibRef
Sun, Y.X.[Ying-Xiang],
Abeywickrama, S.[Samith],
Jayasinghe, L.[Lahiru],
Yuen, C.[Chau],
Chen, J.J.[Jia-Jia],
Zhang, M.[Meng],
Micro-Doppler Signature-Based Detection, Classification, and
Localization of Small UAV With Long Short-Term Memory Neural Network,
GeoRS(59), No. 8, August 2021, pp. 6285-6300.
IEEE DOI
2108
Surveillance, Propellers, Feature extraction,
Unmanned aerial vehicles, Radar cross-sections, Receivers,
unmanned aerial vehicle (UAV)
BibRef
Park, G.[Giseo],
Choi, S.B.[Seibum B.],
An Integrated Observer for Real-Time Estimation of Vehicle Center of
Gravity Height,
ITS(22), No. 9, September 2021, pp. 5660-5671.
IEEE DOI
2109
Tires, Observers, Real-time systems, Rollover, Sensors, Wheels,
Center of gravity height, real-time estimation,
Lyapunov stability
BibRef
Espinosa, J.E.[Jorge E.],
Velastín, S.A.[Sergio A.],
Branch, J.W.[John W.],
Detection of Motorcycles in Urban Traffic Using Video Analysis: A
Review,
ITS(22), No. 10, October 2021, pp. 6115-6130.
IEEE DOI
2110
Motorcycles, Feature extraction, Head, Safety, Roads, Cameras, Shape,
Vulnerable road users (VRU), motorcycle detection
BibRef
He, P.[Pan],
Wu, A.[Aotian],
Huang, X.H.[Xiao-Hui],
Scott, J.[Jerry],
Rangarajan, A.[Anand],
Ranka, S.[Sanjay],
Truck and Trailer Classification With Deep Learning Based Geometric
Features,
ITS(22), No. 12, December 2021, pp. 7782-7791.
IEEE DOI
2112
Machine learning, Transportation, Feature extraction, Axles, Shape,
Semantics, Truck and trailer classification, deep learning,
intelligent transportation system
BibRef
Lu, L.[Lei],
Wang, P.[Ping],
Huang, H.[Hua],
A Large-Scale Frontal Vehicle Image Dataset for Fine-Grained Vehicle
Categorization,
ITS(23), No. 3, March 2022, pp. 1818-1828.
IEEE DOI
2203
Task analysis, Automobiles, Computational modeling, Cameras,
Surveillance, Image Dataset, fine-grained, vehicle categorization
BibRef
Zhang, X.Y.[Xin-Yu],
Zhang, R.F.[Ru-Feng],
Cao, J.W.[Jie-Wei],
Gong, D.[Dong],
You, M.Y.[Ming-Yu],
Shen, C.H.[Chun-Hua],
Part-Guided Attention Learning for Vehicle Instance Retrieval,
ITS(23), No. 4, April 2022, pp. 3048-3060.
IEEE DOI
2204
Feature extraction, Automobiles, Data mining, Visualization,
Measurement, Training, Detectors, Vehicle instance retrieval,
top-down attention
BibRef
Mu, C.Y.[Ching-Yun],
Kung, P.[Pin],
Chen, C.F.[Chien-Fu],
Chuang, S.C.[Shu-Cheng],
Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Rababaah, A.R.[Aaron Rasheed],
Deep learning solution for machine vision problem of vehicle body
damage classification,
IJCVR(12), No. 4, 2022, pp. 426-442.
DOI Link
2207
BibRef
Geng, Q.C.[Qi-Chuan],
Zhang, H.[Hong],
Lu, F.X.[Fei-Xiang],
Huang, X.Y.[Xin-Yu],
Wang, S.[Sen],
Zhou, Z.[Zhong],
Yang, R.G.[Rui-Gang],
Part-Level Car Parsing and Reconstruction in Single Street View
Images,
PAMI(44), No. 8, August 2022, pp. 4291-4305.
IEEE DOI
2207
Automobiles, Shape, Image reconstruction, Semantics, Annotations,
Car parsing and reconstruction, part segmentation,
part-level car dataset
BibRef
Lu, L.[Lei],
Wang, P.[Ping],
Cao, Y.J.[Yi-Jie],
A novel part-level feature extraction method for fine-grained vehicle
recognition,
PR(131), 2022, pp. 108869.
Elsevier DOI
2208
Fine-grained recognition, Part-level feature extraction,
Feature grouping, Feature fusion
BibRef
Boukerche, A.[Azzedine],
Ma, X.R.[Xi-Ren],
A Novel Smart Lightweight Visual Attention Model for Fine-Grained
Vehicle Recognition,
ITS(23), No. 8, August 2022, pp. 13846-13862.
IEEE DOI
2208
Feature extraction, Detectors, Vehicle detection, Object detection,
Data mining, Task analysis, Standards,
vehicle detection and fine-grained recognition
BibRef
Park, J.K.[Jeong-Ki],
Choi, I.O.[In-Oh],
Kim, K.T.[Kyung-Tae],
Length Prediction of Moving Vehicles Using a Commercial FMCW Radar,
ITS(23), No. 9, September 2022, pp. 14833-14845.
IEEE DOI
2209
Radar, Clutter, Radar clutter, Length measurement,
Radar applications, Magnetic sensors, Feature extraction, vehicle length
BibRef
Li, D.W.[De-Wang],
Huang, H.[Hua],
Few-Shot Class-Incremental Learning via Compact and Separable
Features for Fine-Grained Vehicle Recognition,
ITS(23), No. 11, November 2022, pp. 21418-21429.
IEEE DOI
2212
Feature extraction, Training, Power capacitors, Adaptation models,
Task analysis, Image recognition, Measurement,
linear discriminant analysis
BibRef
Duan, J.X.[Jun-Xian],
Wu, X.[Xiang],
Hu, Y.[Yibo],
Fu, C.Y.[Chao-You],
Wang, Z.[Zi],
He, R.[Ran],
Iterative embedding distillation for open world vehicle recognition,
PR(135), 2023, pp. 109140.
Elsevier DOI
2212
Vehicle reidentification, Iterative embedding distillation
BibRef
Zhou, W.[Wei],
Liu, Y.Q.[Yu-Qing],
Wang, C.[Chen],
Zhan, Y.F.[Yun-Fei],
Dai, Y.[Yulu],
Wang, R.[Ruiyu],
An Automated Learning Framework With Limited and Cross-Domain Data
for Traffic Equipment Detection from Surveillance Videos,
ITS(23), No. 12, December 2022, pp. 24891-24903.
IEEE DOI
2212
Surveillance, Videos, Task analysis, Cameras, Internet, Object detection,
Roads, Traffic equipment detection, limited and cross-domain data
BibRef
Amirkhani, A.[Abdollah],
Barshooi, A.H.[Amir Hossein],
DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging
Conditions,
ITS(24), No. 1, January 2023, pp. 541-553.
IEEE DOI
2301
Feature extraction, Solid modeling, Automobiles, Image recognition,
Video recording, License plate recognition, Surveillance,
vehicle make and model recognition (VMMR)
BibRef
Wang, X.H.[Xuan-Hong],
Yang, S.Y.[Shi-Yu],
Xiao, Y.[Yun],
Zheng, X.[Xia],
Gao, S.[Shuai],
Zhou, J.C.[Jin-Cheng],
A vehicle classification model based on deep active learning,
PRL(171), 2023, pp. 84-91.
Elsevier DOI
2306
BibRef
Wang, X.K.[Xin-Kuang],
Li, W.J.[Wen-Jing],
Wu, Z.C.[Zhong-Cheng],
CarDD: A New Dataset for Vision-Based Car Damage Detection,
ITS(24), No. 7, July 2023, pp. 7202-7214.
IEEE DOI
2307
Automobiles, Task analysis, Object detection, Image segmentation,
Annotations, Insurance, Magnetic fields, Car damage, new dataset,
salient object detection (SOD)
BibRef
Li, Q.W.[Qian-Wen],
Li, X.P.[Xiao-Peng],
Yao, H.[Handong],
Liang, Z.H.[Zhao-Hui],
Xie, W.J.[Wei-Jun],
Automated Vehicle Identification Based on Car-Following Data With
Machine Learning,
ITS(24), No. 12, December 2023, pp. 13893-13902.
IEEE DOI
2312
BibRef
Zhang, H.C.[Han-Cheng],
Zhou, W.[Wei],
Liu, G.[Gang],
Wang, Z.X.[Zuo-Xv],
Qian, Z.D.[Zhen-Dong],
Fine-Grained Vehicle Make and Model Recognition Framework Based on
Magnetic Fingerprint,
ITS(25), No. 8, August 2024, pp. 8460-8472.
IEEE DOI
2408
Fingerprint recognition, Magnetic fields, Feature extraction,
Data models, Magnetic resonance imaging, Data mining, AdaBoostSVM
BibRef
Liu, D.[Dichao],
Progressive Multi-Task Anti-Noise Learning and Distilling Frameworks
for Fine-Grained Vehicle Recognition,
ITS(25), No. 9, September 2024, pp. 10667-10678.
IEEE DOI Code:
WWW Link.
2409
Noise, Task analysis, Image recognition, Multitasking, Training,
Noise measurement, Accuracy, Fine-grained vehicle recognition,
object recognition
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A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in
Satellite Images,
ITS(25), No. 10, October 2024, pp. 13323-13338.
IEEE DOI
2410
Containers, Satellite images, Object detection, Vehicle detection,
Detectors, Public healthcare, Proposals, model ensemble
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Knowledge-Distillation-Based Label Smoothing for Fine-Grained
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RWSurvil24(330-340)
IEEE DOI
2404
Training, Deep learning, Smoothing methods, Surveillance, Semantics
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Stanek, R.[Roman],
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Real-Time Wheel Detection and Rim Classification in Automotive
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ICIP23(1410-1414)
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2312
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Chen, W.T.[Wei-Ting],
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Ding, J.J.[Jian-Jiun],
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RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on
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2211
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Du, Y.H.[Yun-Hao],
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Chen, H.[Hong],
OMG: Observe Multiple Granularities for Natural Language-Based
Vehicle Retrieval,
AICity22(3123-3132)
IEEE DOI
2210
Representation learning, Visualization, Smart cities, Surveillance,
Semantics, Feature extraction
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Lee, G.[Gyunpyo],
Kim, T.[Taesu],
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GP22: A Car Styling Dataset for Automotive Designers,
CVFAD22(2267-2271)
IEEE DOI
2210
Image segmentation, Annotations, Feature detection, Scalability,
Taxonomy, Rendering (computer graphics)
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Ding, J.L.[Jia-Li],
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Chen, Q.X.[Qi-Xin],
Yuan, Z.J.[Ze-Jian],
Shang, Y.Y.[Yuan-Yuan],
Visual Saliency Oriented Vehicle Scale Estimation,
ICPR21(1867-1873)
IEEE DOI
2105
Visualization, Image resolution, Algebra, Estimation, Transportation,
Object detection, Visual systems
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Patané, M.[Marco],
Fusiello, A.[Andrea],
Vehicle Classification from Profile Measures,
ICPR21(6656-6663)
IEEE DOI
2105
Measurement, Image color analysis,
Particle measurements, Real-time systems, Sensors, Data mining
BibRef
Liu, Z.,
Lu, F.,
Wang, P.,
Miao, H.,
Zhang, L.,
Yang, R.,
Zhou, B.,
3D Part Guided Image Editing for Fine-Grained Object Understanding,
CVPR20(11333-11342)
IEEE DOI
2008
Automobiles, Solid modeling,
Image segmentation, Vehicle dynamics, Training
BibRef
Ding, Y.,
Zhou, Y.,
Zhu, Y.,
Ye, Q.,
Jiao, J.,
Selective Sparse Sampling for Fine-Grained Image Recognition,
ICCV19(6598-6607)
IEEE DOI
2004
compressed sensing, convolutional neural nets,
feature extraction, image capture, image recognition, Automobiles
BibRef
Hu, H.,
Cai, Q.,
Wang, D.,
Lin, J.,
Sun, M.,
Kraehenbuehl, P.,
Darrell, T.J.,
Yu, F.,
Joint Monocular 3D Vehicle Detection and Tracking,
ICCV19(5389-5398)
IEEE DOI
2004
estimation theory, extrapolation, feature extraction,
image capture, image matching, image motion analysis,
Videos
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Behrendt, K.,
Boxy Vehicle Detection in Large Images,
CVRSUAD19(840-846)
IEEE DOI
2004
Dataset, Vehicles.
WWW Link. cameras, image resolution, image segmentation, object detection,
road vehicles, traffic engineering computing, individual teams,
dataset
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Song, X.B.[Xi-Bin],
Wang, P.[Peng],
Zhou, D.F.[Ding-Fu],
Zhu, R.[Rui],
Guan, C.Y.[Chen-Ye],
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Su, H.[Hao],
Li, H.D.[Hong-Dong],
Yang, R.G.[Rui-Gang],
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for
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IEEE DOI
2002
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Camacho, C.[Camilo],
Pedraza, C.[César],
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An Artificial Vision Based Method for Vehicle Detection and
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IbPRIA19(II:394-403).
Springer DOI
1910
BibRef
Reddy, N.D.,
Vo, M.,
Narasimhan, S.G.,
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D
Reconstruction of Vehicles,
CVPR18(1906-1915)
IEEE DOI
1812
Cameras, Automobiles,
Image reconstruction, Detectors, Shape, Proposals
BibRef
Tian, Y.,
Zhang, W.,
Zhang, Q.,
Lu, G.,
Wu, X.,
Selective Multi-Convolutional Region Feature Extraction based
Iterative Discrimination CNN for Fine-Grained Vehicle Model
Recognition,
ICPR18(3279-3284)
IEEE DOI
1812
Feature extraction, Convolution, Computational modeling,
Image recognition,
BibRef
Hasnat, A.,
Shvai, N.,
Meicler, A.,
Maarek, P.,
Nakib, A.,
New Vehicle Classification Method Based on Hybrid Classifiers,
ICIP18(3084-3088)
IEEE DOI
1809
Cameras, Boosting, Support vector machines, Fuses, Axles,
Task analysis, Meters, Vehicle Classification,
Gradient Boosting
BibRef
Sommer, L.,
Schumann, A.,
Müller, T.,
Schuchert, T.,
Beyerer, J.,
Flying object detection for automatic UAV recognition,
AVSS17(1-6)
IEEE DOI
1806
autonomous aerial vehicles, image recognition, neurocontrollers,
object detection, robot vision, video cameras,
Unmanned aerial vehicles
BibRef
Schumann, A.,
Sommer, L.,
Klatte, J.,
Schuchert, T.,
Beyerer, J.,
Deep cross-domain flying object classification for robust UAV
detection,
AVSS17(1-6)
IEEE DOI
1806
image classification, image sequences,
learning (artificial intelligence), mobile robots, neural nets,
Robustness
BibRef
Unlu, E.,
Zenou, E.,
Riviere, N.,
Ordered minimum distance bag-of-words approach for aerial object
identification,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, object detection,
object recognition, SURF based object recognition,
Visualization
BibRef
Aker, C.,
Kalkan, S.,
Using deep networks for drone detection,
AVSS17(1-6)
IEEE DOI
1806
autonomous aerial vehicles, convolution, image sequences,
neural nets, object detection, video signal processing,
Training
BibRef
Saqib, M.,
Daud Khan, S.,
Sharma, N.,
Blumenstein, M.,
A study on detecting drones using deep convolutional neural networks,
AVSS17(1-5)
IEEE DOI
1806
autonomous aerial vehicles, convolution,
learning (artificial intelligence), neural nets,
Training
BibRef
Selbes, B.,
Sert, M.,
Multimodal vehicle type classification using convolutional neural
network and statistical representations of MFCC,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, feature selection, image classification,
image fusion, image representation,
Visualization
BibRef
Ma, C.,
Liu, D.,
Peng, X.,
Wu, F.,
Surveillance video coding with vehicle library,
ICIP17(270-274)
IEEE DOI
1803
Cameras, Encoding, Feature extraction, Indexes, Libraries,
Surveillance, Video coding, HEVC, Inter prediction,
Vehicle library
BibRef
Liu, H.,
Tian, Y.,
Wang, Y.,
Pang, L.,
Huang, T.,
Deep Relative Distance Learning:
Tell the Difference between Similar Vehicles,
CVPR16(2167-2175)
IEEE DOI
1612
Dataset:
See also PKU VehicleID Dataset.
BibRef
Zwemer, M.H.,
Brouwers, G.M.Y.E.,
Wijnhoven, R.G.J.[Rob G.J.],
de With, P.H.N.[Peter H.N.],
Semi-automatic Training of a Vehicle Make and Model Recognition System,
CIAP17(II:321-332).
Springer DOI
1711
BibRef
Jung, H.,
Choi, M.K.,
Jung, J.,
Lee, J.H.,
Kwon, S.,
Jung, W.Y.,
ResNet-Based Vehicle Classification and Localization in Traffic
Surveillance Systems,
Traffic17(934-940)
IEEE DOI
1709
Feature extraction, Proposals
BibRef
Kim, P.K.,
Lim, K.T.,
Vehicle Type Classification Using Bagging and Convolutional Neural
Network on Multi View Surveillance Image,
Traffic17(914-919)
IEEE DOI
1709
Automobiles, Bagging, Error analysis, Machine learning,
Surveillance, Training
BibRef
Theagarajan, R.,
Pala, F.,
Bhanu, B.,
EDeN: Ensemble of Deep Networks for Vehicle Classification,
Traffic17(906-913)
IEEE DOI
1709
Automobiles, Cameras, Radar tracking,
Surveillance, Traffic control, Training
BibRef
Tafazzoli, F.,
Frigui, H.,
Nishiyama, K.,
A Large and Diverse Dataset for Improved Vehicle Make and Model
Recognition,
Traffic17(874-881)
IEEE DOI
1709
Automobiles, Cameras, Computational modeling, Licenses, Robustness,
Surveillance, Three-dimensional, displays
BibRef
Ma, K.[Kaili],
Zhang, J.[Jun],
Wang, F.L.[Feng-Lei],
Tu, D.[Dan],
Li, S.H.[Shuo-Hao],
Fine-grained object detection based on self-adaptive anchors,
ICIVC17(78-82)
IEEE DOI
1708
Approximation algorithms, Automobiles, Clustering algorithms,
Feature extraction, Object detection, Proposals, Training,
convolutional neural network, faster R-CNN, fine-grained object,
self-adaptive, anchors
BibRef
Tafazzoli, F.,
Frigui, H.,
Vehicle make and model recognition using local features and logo
detection,
ISIVC16(353-358)
IEEE DOI
1704
Detectors
BibRef
Li, B.[Bo],
Wu, T.F.[Tian-Fu],
Xiong, C.M.[Cai-Ming],
Zhu, S.C.[Song-Chun],
Recognizing Car Fluents from Video,
CVPR16(3803-3812)
IEEE DOI
1612
Time varying states in dynamic scenes. Details.
BibRef
Boyle, J.[Jonathan],
Ferryman, J.M.[James M.],
Vehicle subtype, make and model classification from side profile
video,
AVSS15(1-6)
IEEE DOI
1511
Cameras
BibRef
Khurram, I.[Imran],
Fraz, M.M.[Muhammad Moazam],
Shahzad, M.S.[Muhammad Saquib],
Detailed Sentence Generation Architecture for Image Semantics
Description,
ISVC18(423-432).
Springer DOI
1811
BibRef
Fraz, M.M.[Muhammad Moazam],
Edirisinghe, E.A.[Eran A.],
Sarfraz, M.S.[M. Saquib],
Mid-level-Representation Based Lexicon for Vehicle Make and Model
Recognition,
ICPR14(393-398)
IEEE DOI
1412
Computational modeling
BibRef
Dong, Z.[Zhen],
Jia, Y.D.[Yun-De],
Vehicle type classification using distributions of structural and
appearance-based features,
ICIP13(4321-4324)
IEEE DOI
1402
Vehicle type classification
BibRef
Abdel Maseeh, M.[Meena],
Badreldin, I.[Islam],
Abdelkader, M.F.[Mohamed F.],
El Saban, M.[Motaz],
Car Make and Model recognition combining global and local cues,
ICPR12(910-913).
WWW Link.
1302
BibRef
Pearce, G.,
Pears, N.E.,
Automatic make and model recognition from frontal images of cars,
AVSBS11(373-378).
IEEE DOI
1111
BibRef
Negri, P.[Pablo],
Clady, X.[Xavier],
Milgram, M.[Maurice],
Poulenard, R.[Raphael],
An Oriented-Contour Point Based Voting Algorithm for Vehicle Type
Classification,
ICPR06(I: 574-577).
IEEE DOI
0609
BibRef
Ozcanli, O.C.[Ozge C.],
Tamrakar, A.[Amir],
Kimia, B.B.[Benjamin B.],
Augmenting Shape with Appearance in Vehicle Category Recognition,
CVPR06(I: 935-942).
IEEE DOI
0606
BibRef
Petrovic, V.S.,
Cootes, T.F.,
Analysis of Features for Rigid Structure Vehicle Type Recognition,
BMVC04(xx-yy).
HTML Version.
0508
BibRef
And:
Vehicle type recognition with match refinement,
ICPR04(III: 95-98).
IEEE DOI
0409
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Pose .