Abstract
Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.
- 2019. ITS Canada. Retrieved November 2019 from https://www.itscanada.ca/it/society/index.html.Google Scholar
- David Arthur and Sergei Vassilvitskii. 2007. K-Means++: The advantages of careful seeding. In Proc. ACM-SIAM SODA. 1027--1035.Google Scholar
- Y. Bai, Y. Lou, F. Gao, S. Wang, Y. Wu, and L. Duan. 2018. Group-sensitive triplet embedding for vehicle reidentification. IEEE Trans. Multimedia 20, 9 (2018), 2385--2399.Google ScholarDigital Library
- E. Bochinski, V. Eiselein, and T. Sikora. 2017. High-speed tracking-by-detection without using image information. In Proc. IEEE AVSS. 1--6.Google Scholar
- Azzedine Boukerche, Abdul Jabbar Siddiqui, and Abdelhamid Mammeri. 2017. Automated vehicle detection and classification: Models, methods, and techniques. ACM Comput. Surv. 50, 5 (2017), 62.Google Scholar
- Azzedine Boukerche and Jiahao Wang. 2020. Machine learning-based traffic prediction models for intelligent transportation systems. Comput. Netw. 181 (2020), 107530.Google ScholarCross Ref
- Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993. Signature verification using a “Siamese” time delay neural network. In Proc. NIPS. 737--744.Google ScholarCross Ref
- Zhaowei Cai, Quanfu Fan, Rogerio S. Feris, and Nuno Vasconcelos. 2016. A unified multi-scale deep convolutional neural network for fast object detection. In Proc. ECCV. 354--370.Google ScholarCross Ref
- P. Chen, P. Li, Q. Li, and D. Zhang. 2019. Semi-supervised fine-grained image categorization using transfer learning with hierarchical multi-scale adversarial networks. IEEE Access 7 (2019), 118650--118668.Google ScholarCross Ref
- Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273--297.Google ScholarCross Ref
- Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray. 2004. Visual categorization with bags of keypoints. In ECCV Workshop on Stat. Learn. in Comp. Vision. 1--2.Google Scholar
- Z. Deng, H. Sun, S. Zhou, J. Zhao, and H. Zou. 2017. Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 10, 8 (2017), 3652--3664.Google ScholarCross Ref
- Robert Desimone and John Duncan. 1995. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 1 (1995), 193--222.Google ScholarCross Ref
- S. Du, M. Ibrahim, M. Shehata, and W. Badawy. 2013. Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Trans. Circ. Syst. Video Technol. 23, 2 (2013), 311--325.Google ScholarDigital Library
- Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. 2019. CenterNet: Keypoint Triplets for Object Detection. Retrieved November 2019 from https://arxiv.org/abs/1904.08189.Google Scholar
- M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2010. The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 2 (2010), 303--338.Google ScholarDigital Library
- Jie Fang, Yu Zhou, Yao Yu, and Sidan Du. 2017. Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture. IEEE Trans. Intell. Transp. Syst. 18, 7 (2017), 1782--1792.Google ScholarDigital Library
- Pedro F. Felzenszwalb and Daniel P. Huttenlocher. 2004. Efficient graph-based image segmentation. Int. J. Comput. Vis. 59, 2 (2004), 167--181.Google ScholarDigital Library
- Jianlong Fu, Heliang Zheng, and Tao Mei. 2017. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In Proc. IEEE CVPR. 4476--4484.Google ScholarCross Ref
- Yang Gao, Shouyan Guo, Kaimin Huang, Jiaxin Chen, Qian Gong, Yang Zou, Tong Bai, and Gary Overett. 2017. Scale optimization for full-image-CNN vehicle detection. In Proc. IEEE IV. 785--791.Google ScholarCross Ref
- Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. 2013. Vision meets robotics: The KITTI dataset. Int. J. Robot. Res. 32, 11 (2013), 1231--1237.Google ScholarDigital Library
- Ross Girshick. 2015. Fast r-cnn. In Proc. IEEE ICCV. 1440--1448.Google ScholarDigital Library
- R. Girshick, J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. IEEE CVPR. 580--587.Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proc. NIPS. 2672--2680.Google ScholarDigital Library
- Matthieu Guillaumin, Daniel Küttel, and Vittorio Ferrari. 2014. Imagenet auto-annotation with segmentation propagation. Int. J. Comput. Vis. 110, 3 (2014), 328--348.Google ScholarDigital Library
- Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, and Hanqing Lu. 2018. Learning coarse-to-fine structured feature embedding for vehicle re-identification. In Proc. AAAI Conf. Artif. Intell.Google Scholar
- H. Guo, K. Zhu, M. Tang, and J. Wang. 2019. Two-level attention network with multi-grain ranking loss for vehicle re-identification. IEEE Trans. Image Process. 28, 9 (2019), 4328--4338.Google ScholarCross Ref
- Song Han, Huizi Mao, and William J. Dally. 2015. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. Retrieved November 2018 from http://arxiv.org/abs/1510.00149.Google Scholar
- Li-Ying Hao, Jie Li, and Ge Guo. 2020. A multi-target corner pooling-based neural network for vehicle detection. Neural Comput. Appl. 32, 18 (2020), 14497--14506.Google ScholarCross Ref
- Bing He, Jia Li, Yifan Zhao, and Yonghong Tian. 2019. Part-regularized near-duplicate vehicle re-identification. In Proc. IEEE CVPR. 3997--4005.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proc. ECCV. 770--778.Google ScholarCross Ref
- S. He, H. Luo, W. Chen, M. Zhang, Y. Zhang, F. Wang, H. Li, and W. Jiang. 2020. Multi-domain learning and identity mining for vehicle re-identification. In Proc. IEEE CVPRW. 2485--2493.Google Scholar
- Y. He and L. Li. 2018. A novel multi-source vehicle detection algorithm based on deep learning. In Proc. IEEE ICSIP. 979--982.Google Scholar
- Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017. In Defense of the Triplet Loss for Person Re-Identification. Retrieved November 2019 from http://arxiv.org/abs/1703.07737.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- Qichang Hu, Huibing Wang, Teng Li, and Chunhua Shen. 2017. Deep cnns with spatially weighted pooling for fine-grained car recognition. IEEE Trans. Intell. Transp. Syst. 18, 11 (2017), 3147--3156.Google ScholarDigital Library
- G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger. 2017. Densely connected convolutional networks. In Proc. IEEE CVPR. 2261--2269.Google Scholar
- Kun Huang and Bailing Zhang. 2016. Fine-grained vehicle recognition by deep convolutional neural network. In Proc. CISP-BMEI. 465--470.Google ScholarCross Ref
- Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Retrieved November 2017 from http://arxiv.org/abs/1502.03167.Google Scholar
- Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. 2015. Spatial Transformer Networks. Retrieved November 2019 from http://arxiv.org/abs/1506.02025.Google Scholar
- Saumya Jetley, Nicholas A. Lord, Namhoon Lee, and Philip H. S. Torr. 2018. Learn To Pay Attention. Retrieved September 2018 from http://arxiv.org/abs/1804.02391.Google Scholar
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proc. ACM Int. Conf. Multimed.675--678.Google ScholarDigital Library
- N. Jiang, Y. Xu, Z. Zhou, and W. Wu. 2018. Multi-attribute driven vehicle re-identification with spatial-temporal re-ranking. In Proc. IEEE ICIP. 858--862.Google Scholar
- Q. Jiang, L. Cao, M. Cheng, C. Wang, and J. Li. 2015. Deep neural networks-based vehicle detection in satellite images. In Proc. ISBB. 184--187.Google Scholar
- Sultan Daud Khan and Habib Ullah. 2019. A survey of advances in vision-based vehicle re-identification. Comput. Vis. Image Underst. 182 (2019), 50--63.Google ScholarDigital Library
- P. Khorramshahi, A. Kumar, N. Peri, S. S. Rambhatla, J. Chen, and R. Chellappa. 2019. A dual-path model with adaptive attention for vehicle re-identification. In Proc. IEEE ICCV. 6131--6140.Google Scholar
- S. Kong and C. Fowlkes. 2017. Low-rank bilinear pooling for fine-grained classification. In Proc. IEEE CVPR. 7025--7034.Google Scholar
- Jonathan Krause, Hailin Jin, Jianchao Yang, and Li Fei-Fei. 2015. Fine-grained recognition without part annotations. In Proc. IEEE CVPR. 5546--5555.Google ScholarCross Ref
- Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013. 3d object representations for fine-grained categorization. In Proc. IEEE CVPR. 554--561.Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 6 (2017), 84--90.Google ScholarDigital Library
- R. Kuma, E. Weill, F. Aghdasi, and P. Sriram. 2019. Vehicle re-identification: An efficient baseline using triplet embedding. In Proc. IEEE IJCNN. 1--9.Google Scholar
- Hei Law and Jia Deng. 2018. Cornernet: Detecting objects as paired keypoints. In Proc. ECCV. 734--750.Google ScholarCross Ref
- Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE Proc. IRE* (through 1962) 86, 11 (1998), 2278--2324.Google Scholar
- Suichan Li and Feng Chen. 2018. 3D-DETNet: A single stage video-based vehicle detector. In Proc. IWPR, Vol. 10828. 60--66.Google Scholar
- X. Li, L. Yu, D. Chang, Z. Ma, and J. Cao. 2019. Dual cross-entropy loss for small-sample fine-grained vehicle classification. IEEE Trans. Veh. Technol. 68, 5 (2019), 4204--4212.Google ScholarCross Ref
- Y. Li, F. Naeimipoor, and A. Boukerche. 2014. Video dissemination protocols in urban vehicular ad hoc network: A performance evaluation study. In Proc. IEEE WCNC. 2611--2616.Google Scholar
- Jin-Fu Lin, Yen-Liang Lin, Erh-Kan King, Hung-Ting Su, and Winston H Hsu. 2018. Cross-domain hallucination network for fine-grained object recognition. In Proc. IEEE CVPRW. 1214--1221.Google ScholarCross Ref
- T. Lin, A. RoyChowdhury, and S. Maji. 2018. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 6 (2018), 1309--1322.Google ScholarCross Ref
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proc. ECCV. 740--755.Google Scholar
- C. Liu, H. Xie, Z. Zha, L. Yu, Z. Chen, and Y. Zhang. 2020. Bidirectional attention-recognition model for fine-grained object classification. IEEE Trans. Multimedia 22, 7 (2020), 1785--1795.Google ScholarCross Ref
- H. Liu, Y. Tian, Y. Wang, L. Pang, and T. Huang. 2016. Deep relative distance learning: Tell the difference between similar vehicles. In Proc. IEEE CVPR. 2167--2175.Google Scholar
- K. Liu and G. Mattyus. 2015. Fast multiclass vehicle detection on aerial images. IEEE Geosci. Remote Sens. Lett. 12, 9 (2015), 1938--1942.Google ScholarCross Ref
- Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. 2016. Ssd: Single shot multibox detector. In Proc. ECCV. 21--37.Google Scholar
- Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E. Alsaadi. 2017. A survey of deep neural network architectures and their applications. Neurocomputing 234 (2017), 11--26.Google ScholarCross Ref
- X. Liu, W. Liu, H. Ma, and H. Fu. 2016. Large-scale vehicle re-identification in urban surveillance videos. In Proc. IEEE ICME. 1--6.Google Scholar
- Xinchen Liu, Wu Liu, Tao Mei, and Huadong Ma. 2016. A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In Proc. ECCV, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). 869--884.Google ScholarCross Ref
- X. Liu, W. Liu, T. Mei, and H. Ma. 2018. PROVID: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimedia 20, 3 (2018), 645--658.Google ScholarDigital Library
- Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, and Yuanqing Lin. 2017. Localizing by describing: Attribute-guided attention localization for fine-grained recognition. In Proc. AAAI Conf. Artif. Intell.Google Scholar
- Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou, and Yuanqing Lin. 2016. Fully convolutional attention networks for fine-grained recognition. Retrieved September 2018 from https://arxiv.org/abs/1603.06765.Google Scholar
- Y. Lou, Y. Bai, J. Liu, S. Wang, and L. Duan. 2019. Embedding adversarial learning for vehicle re-identification. IEEE Trans. Image Process. 28, 8 (2019), 3794--3807.Google ScholarCross Ref
- Yihang Lou, Yan Bai, Jun Liu, Shiqi Wang, and Lingyu Duan. 2019. Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. In Proc. IEEE CVPR. 3235--3243.Google ScholarCross Ref
- X. Ma and A. Boukerche. 2020. An AI-based visual attention model for vehicle make and model recognition. In Proc. IEEE ISCC. 1--6.Google Scholar
- Z. Ma, D. Chang, J. Xie, Y. Ding, S. Wen, X. Li, Z. Si, and J. Guo. 2019. Fine-grained vehicle classification with channel max pooling modified CNNs. IEEE Trans. Veh. Technol. 68, 4 (2019), 3224--3233.Google ScholarCross Ref
- Abdelhamid Mammeri, Azzedine Boukerche, and Guangqian Lu. 2014. Lane detection and tracking system based on the MSER algorithm, hough transform and Kalman filter. In Proc. ACM MSWiM. 259--266.Google ScholarDigital Library
- A. Mammeri, E. Khiari, and A. Boukerche. 2014. Road-sign text recognition architecture for intelligent transportation systems. In Proc. IEEE VTC. 1--5.Google Scholar
- A. Mammeri, G. Lu, and A. Boukerche. 2015. Design of lane keeping assist system for autonomous vehicles. In Proc. NTMS. 1--5.Google Scholar
- A. Mammeri, D. Zhou, and A. Boukerche. 2016. Animal-vehicle collision mitigation system for automated vehicles. IEEE Trans. Syst. Man Cybern. Syst. 46, 9 (2016), 1287--1299.Google ScholarCross Ref
- A. Mammeri, D. Zhou, A. Boukerche, and M. Almulla. 2014. An efficient animal detection system for smart cars using cascaded classifiers. In Proc. IEEE ICC. 1854--1859.Google Scholar
- A. Mammeri, T. Zuo, and A. Boukerche. 2014. Extending the detection range of vision-based driver assistance systems application to Pedestrian Protection System. In Proc. IEEE GLOBECOM. 1358--1363.Google Scholar
- A. Mammeri, T. Zuo, and A. Boukerche. 2016. Extending the detection range of vision-based vehicular instrumentation. IEEE Trans. Instrum. Meas. 65, 4 (2016), 856--873.Google ScholarCross Ref
- F. Naeimipoor and A. Boukerche. 2014. A hybrid video dissemination protocol for VANETs. In Proc. IEEE ICC. 112--117.Google Scholar
- Alejandro Newell, Kaiyu Yang, and Jia Deng. 2016. Stacked hourglass networks for human pose estimation. In Proc. ECCV. 483--499.Google ScholarCross Ref
- Jingjing Qian, Wei Jiang, Hao Luo, and Hongyan Yu. 2020. Stripe-based and attribute-aware network: A two-branch deep model for vehicle re-identification. Meas. Sci. Technol. 31, 9 (Jun. 2020), 095401.Google ScholarCross Ref
- J. Zhu, T. Park, P. Isola, and A. A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proc. IEEE ICCV. 2242--2251.Google Scholar
- Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. Retrieved November 2019 from http://arxiv.org/abs/1511.06434.Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proc. IEEE CVPR. 779--788.Google ScholarCross Ref
- Joseph Redmon and Ali Farhadi. 2017. YOLO9000: Better, faster, stronger. In Proc. IEEE CVPR. 7263--7271.Google ScholarCross Ref
- Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. Retrieved November 2018 from https://arxiv.org/abs/1804.02767.Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Proc. NIPS. 91--99.Google Scholar
- E. Ristani and C. Tomasi. 2018. Features for multi-target multi-camera tracking and re-identification. In Proc. IEEE CVPR. 6036--6046.Google Scholar
- Pau Rodríguez, Josep M Gonfaus, Guillem Cucurull, F XavierRoca, and Jordi Gonzalez. 2018. Attend and rectify: A gated attention mechanism for fine-grained recovery. In Proc. ECCV. 349--364.Google ScholarCross Ref
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 3 (2015), 211--252.Google ScholarDigital Library
- Jun Sang, Zhongyuan Wu, Pei Guo, Haibo Hu, Hong Xiang, Qian Zhang, and Bin Cai. 2018. An improved YOLOv2 for vehicle detection. Sensors 18, 12 (2018), 4272.Google ScholarCross Ref
- Y. Shen, T. Xiao, H. Li, S. Yi, and X. Wang. 2017. Learning deep neural networks for vehicle re-ID with visual-spatio-temporal path proposals. In Proc. IEEE ICCV. 1918--1927.Google Scholar
- A. J. Siddiqui, A. Mammeri, and A. Boukerche. 2016. Real-time vehicle make and model recognition based on a bag of SURF features. IEEE Trans. Intell. Transp. Syst. 17, 11 (2016), 3205--3219.Google ScholarDigital Library
- A. Simonelli, F. De Natale, S. Messelodi, and S. R. Bulo. 2018. Increasingly specialized ensemble of convolutional neural networks for fine-grained recognition. In Proc. IEEE ICIP. 594--598.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. Retrieved November 2017 from https://arxiv.org/abs/1409.1556.Google Scholar
- S. Sivaraman and M. M. Trivedi. 2013. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14, 4 (2013), 1773--1795.Google ScholarDigital Library
- J. Sochor, A. Herout, and J. Havel. 2016. BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition. In Proc. IEEE CVPR. 3006--3015.Google Scholar
- Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. 2019. Deep high-resolution representation learning for human pose estimation. In Proc. IEEE CVPR. 5686--5696.Google ScholarCross Ref
- P. Sun and A. Boukerche. 2019. Challenges of designing computer vision-based pedestrian detector for supporting autonomous driving. In Proc. IEEE MASS. 28--36.Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proc. ECCV. 1--9.Google ScholarCross Ref
- Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. 2018. A survey on deep transfer learning. In Proc. ICANN. 270--279.Google ScholarCross Ref
- M. Tan, G. Wang, J. Zhou, Z. Peng, and M. Zheng. 2019. Fine-grained classification via hierarchical bilinear pooling with aggregated slack mask. IEEE Access 7 (2019), 117944--117953.Google ScholarCross Ref
- Zheng Tang, Milind Naphade, Stan Birchfield, Jonathan Tremblay, William Hodge, Ratnesh Kumar, Shuo Wang, and Xiaodong Yang. 2019. PAMTRI: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In Proc. IEEE ICCV. 211--220.Google ScholarCross Ref
- Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David C. Anastasiu, and Jenq-Neng Hwang. 2019. CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification. Retrieved November 2019 from http://arxiv.org/abs/1903.09254.Google Scholar
- Yanling Tian, Weitong Zhang, Qieshi Zhang, Gang Lu, and Xiaojun Wu. 2018. Selective multi-convolutional region feature extraction based iterative discrimination CNN for fine-grained vehicle model recognition. In Proc. IEEE ICPR. 3279--3284.Google ScholarCross Ref
- Thang To, Jonathan Tremblay, Duncan McKay, Yukie Yamaguchi, Kirby Leung, Adrian Balanon, Jia Cheng, William Hodge, and Stan Birchfield. 2018. NDDS: NVIDIA Deep Learning Dataset Synthesizer. Retrieved November 2019 from https://github.com/NVIDIA/Dataset_Synthesizer.Google Scholar
- J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. 2013. Selective search for object recognition. Int. J. Comput. Vis. 104, 2 (2013), 154--171.Google ScholarDigital Library
- R. Wang, M. Almulla, C. Rezende, and A. Boukerche. 2014. Video streaming over vehicular networks by a multiple path solution with error correction. In Proc. IEEE ICC. 580--585.Google Scholar
- Y. Wang, V. I. Morariu, and L. S. Davis. 2018. Learning a discriminative filter bank within a CNN for fine-grained recognition. In Proc. IEEE CVPR. 4148--4157.Google Scholar
- Z. Wang, L. Tang, X. Liu, Z. Yao, S. Yi, J. Shao, J. Yan, S. Wang, H. Li, and X. Wang. 2017. Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In Proc. IEEE ICCV. 379--387.Google Scholar
- C. Wu, C. Liu, C. Chiang, W. Tu, and S. Chien. 2018. Vehicle re-identification with the space-time prior. In Proc. IEEE CVPRW. 121--1217.Google Scholar
- F. Wu, S. Yan, J. S. Smith, and B. Zhang. 2018. Joint semi-supervised learning and re-ranking for vehicle re-identification. In Proc. IEEE ICPR. 278--283.Google Scholar
- L. Wu, Y. Wang, X. Li, and J. Gao. 2019. Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE Trans. Biomed. Circ. Syst. 49, 5 (2019), 1791--1802.Google Scholar
- Zhongyuan Wu, Jun Sang, Qian Zhang, Hong Xiang, Bin Cai, and Xiaofeng Xia. 2019. Multi-scale vehicle detection for foreground-background class imbalance with improved YOLOv2. Sensors 19, 15 (2019), 3336.Google ScholarCross Ref
- Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang. 2015. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In Proc. IEEE CVPR. 842--850.Google Scholar
- H. Xie, A. Boukerche, and A. A. F. Loureiro. 2015. A multipath video streaming solution for vehicular networks with link disjoint and node-disjoint. IEEE Trans. Parallel Distrib. Syst. 26, 12 (2015), 3223--3235.Google ScholarDigital Library
- Saining Xie, Tianbao Yang, Xiaoyu Wang, and Yuanqing Lin. 2015. Hyper-class augmented and regularized deep learning for fine-grained image classification. In Proc. IEEE CVPR. 2645--2654.Google ScholarCross Ref
- Jiaolong Xu, Yiming Nie, Peng Wang, and Antonio M López. 2019. Training a binary weight object detector by knowledge transfer for autonomous driving. In Proc. IEEE ICRA. 2379--2384.Google ScholarCross Ref
- Linjie Yang, Ping Luo, Chen Change Loy, and Xiaoou Tang. 2015. A large-scale car dataset for fine-grained categorization and verification. In Proc. IEEE CVPR. 3973--3981.Google ScholarCross Ref
- M. B. Younes and A. Boukerche. 2017. A vehicular network based intelligent lane change assistance protocol for highways. In Proc. IEEE ICC. 1--6.Google Scholar
- Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, and Xinge You. 2018. Hierarchical bilinear pooling for fine-grained visual recognition. In Proc. ECCV. 595--610.Google ScholarCross Ref
- Sergey Zagoruyko and Nikos Komodakis. [n.d.]. Wide residual networks. Retrieved November 2018 from http://arxiv.org/abs/1605.07146.Google Scholar
- Sergey Zagoruyko and Nikos Komodakis. 2016. Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer. Retrieved November 2018 from http://arxiv.org/abs/1612.03928.Google Scholar
- M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus. 2010. Deconvolutional networks. In Proc. IEEE CVPR. 2528--2535.Google Scholar
- Ning Zhang, Jeff Donahue, Ross Girshick, and Trevor Darrell. 2014. Part-based R-CNNs for fine-grained category detection. In Proc. ECCV. 834--849.Google ScholarCross Ref
- Xiaopeng Zhang, Hongkai Xiong, Wengang Zhou, Weiyao Lin, and Qi Tian. 2016. Picking deep filter responses for fine-grained image recognition. In Proc. IEEE CVPR. 1134--1142.Google ScholarCross Ref
- Xiaofan Zhang, Feng Zhou, Yuanqing Lin, and Shaoting Zhang. 2016. Embedding label structures for fine-grained feature representation. In Proc. IEEE CVPR. 1114--1123.Google ScholarCross Ref
- Y. Zhang, D. Liu, and Z. Zha. 2017. Improving triplet-wise training of convolutional neural network for vehicle re-identification. In Proc. IEEE ICME. 1386--1391.Google Scholar
- Bo Zhao, Xiao Wu, Jiashi Feng, Qiang Peng, and Shuicheng Yan. 2017. Diversified visual attention networks for fine-grained object classification. IEEE Trans. Multimedia 19, 6 (2017), 1245--1256.Google ScholarDigital Library
- Dongbin Zhao, Yaran Chen, and Le Lv. 2017. Deep reinforcement learning with visual attention for vehicle classification. IEEE Trans. Cogn. Develop. Syst. 9, 4 (2017), 356--367.Google ScholarCross Ref
- Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo. 2017. Learning multi-attention convolutional neural network for fine-grained image recognition. In Proc. IEEE ICCV, Vol. 6.Google ScholarCross Ref
- H. Zheng, J. Fu, Z. Zha, J. Luo, and T. Mei. 2020. Learning rich part hierarchies with progressive attention networks for fine-grained image recognition. IEEE Trans. Image Process. 29 (2020), 476--488.Google ScholarDigital Library
- Z. Zheng, L. Zheng, and Y. Yang. 2017. Unlabeled samples generated by GAN improve the person re-identification baseline in Vitro. In Proc. IEEE ICCV. 3774--3782.Google Scholar
- Weilin Zhong, Linfeng Jiang, Tao Zhang, Jinsheng Ji, and Huilin Xiong. 2018. A multi-part convolutional attention network for fine-grained image recognition. In Proc. IEEE ICPR. 1857--1862.Google ScholarCross Ref
- Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. 2020. Random erasing data augmentation. In Proc. AAAI Conf. Artif. Intell.13001--13008.Google ScholarCross Ref
- Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In Proc. IEEE CVPR. 2921--2929.Google ScholarCross Ref
- Y. Zhou, L. Liu, and L. Shao. 2018. Vehicle re-identification by deep hidden multi-view inference. IEEE Trans. Image Process. 27, 7 (2018), 3275--3287.Google ScholarCross Ref
- Y. Zhouy and L. Shao. 2018. Viewpoint-aware attentive multi-view inference for vehicle re-identification. In Proc. IEEE CVPR. 6489--6498.Google Scholar
- Jianqing Zhu, Yongzhao Du, Yang Hu, Lixin Zheng, and Canhui Cai. 2019. VRSDNet: Vehicle re-identification with a shortly and densely connected convolutional neural network. Multimedia Tools Appl. 78, 20 (2019), 29043--29057.Google ScholarCross Ref
- J. Zhu, H. Zeng, J. Huang, S. Liao, Z. Lei, C. Cai, and L. Zheng. 2020. Vehicle re-identification using quadruple directional deep learning features. IEEE Trans. Intell. Transp. Syst. 21, 1 (2020), 410--420.Google ScholarCross Ref
Index Terms
- Vision-based Autonomous Vehicle Recognition: A New Challenge for Deep Learning-based Systems
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