16.7.2.1.2 Vehicle Make or Model or Type Recogniton

Chapter Contents (Back)
Vehicle Recognition. Vehicle Detection. Fine-Grained Vehicle.
See also Vehicle Tracking, Re-Identification. Includes drone detection.
See also Vehicle Pose.

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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Vision-based bicycle/motorcycle classification,
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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
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Earlier:
How current BNs fail to represent evolvable pattern recognition problems and a proposed solution,
ICPR08(1-4).
IEEE DOI 0812
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Earlier:
Bayesian based 3D shape reconstruction from video,
ICIP08(1152-1155).
IEEE DOI 0810
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Earlier:
Incremental Vehicle 3-D Modeling from Video,
ICPR06(III: 272-275).
IEEE DOI 0609
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Zhang, B.,
Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles,
ITS(14), No. 1, March 2013, pp. 322-332.
IEEE DOI 1303
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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., Ying, C., Lin, C., Liu, S., Li, W.,
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
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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
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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
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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
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Stanek, R.[Roman], Kerepecký, T.[Tomáš], Novozámský, A.[Adam], Šroubek, F.[Filip], Zitová, B.[Barbara], Flusser, J.[Jan],
Real-Time Wheel Detection and Rim Classification in Automotive Production,
ICIP23(1410-1414)
IEEE DOI 2312
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Chen, W.T.[Wei-Ting], Chen, I.H.[I-Hsiang], Yeh, C.Y.[Chih-Yuan], Yang, H.H.[Hao-Hsiang], Chang, H.E.[Hua-En], Ding, J.J.[Jian-Jiun], Kuo, S.Y.[Sy-Yen],
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning,
ECCV22(XIV:427-443).
Springer DOI 2211
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Du, Y.H.[Yun-Hao], Zhang, B.[Binyu], Ruan, X.N.[Xiang-Ning], Su, F.[Fei], Zhao, Z.C.[Zhi-Cheng], 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, Pattern recognition BibRef

Lee, G.[Gyunpyo], Kim, T.[Taesu], Suk, H.J.[Hyeon-Jeong],
GP22: A Car Styling Dataset for Automotive Designers,
CVFAD22(2267-2271)
IEEE DOI 2210
Image segmentation, Annotations, Feature detection, Scalability, Taxonomy, Rendering (computer graphics), Pattern recognition BibRef

Ding, J.L.[Jia-Li], Liu, T.[Tie], 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 BibRef

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 BibRef

Behrendt, K.,
Boxy Vehicle Detection in Large Images,
CVRSUAD19(840-846)
IEEE DOI 2004
Dataset, Vehicles.
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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], Dai, Y.C.[Yu-Chao], Su, H.[Hao], Li, H.D.[Hong-Dong], Yang, R.G.[Rui-Gang],
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving,
CVPR19(5447-5457).
IEEE DOI 2002
BibRef

Camacho, C.[Camilo], Pedraza, C.[César], Higuera, C.[Carolina],
An Artificial Vision Based Method for Vehicle Detection and Classification in Urban Traffic,
IbPRIA19(II:394-403).
Springer DOI 1910
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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, Pattern 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
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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, Pattern recognition, 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, Pattern recognition, 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).
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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 .


Last update:Mar 16, 2024 at 20:36:19