Cited By
View all- Rong LXu YZhou XHan LLi LPan X(2021)A vehicle re-identification framework based on the improved multi-branch feature fusion networkScientific Reports10.1038/s41598-021-99646-611:1Online publication date: 12-Oct-2021
A Network Combining Local Features and Attention Mechanisms for Vehicle Re-Identification
Authors: 
Linghui Li, Xiaohui Zhang, Yan XuAuthors Info & Claims
Linghui Li, Xiaohui Zhang, Yan XuAuthors Info & ClaimsPages 47 - 50
Abstract
Vehicle of the same manufacturer and the same color can only be distinguished by their subtle difference. If these small features, such as stickers on windows and spray paint on cars, can be better used, we can significantly improve the accuracy of vehicle reidentification. This paper aims to develop an effective network combining local features and attention mechanisms for vehicle reidentification. It divides the feature map to enable the network to capture more detailed feature information. At the same time, it uses the attention mechanism to enable the network to focus on the most important part of each branch, effectively eliminating background and other interference, and improving the network performance. Experiments show that this method improves the result of Rank-1 and mAP on two public datasets: VeRi-776 and VRIC.
References
[1]
Xinchen Liu, Wu Liu, Huadong Ma, Huiyuan Fu: Large-scale vehicle re-identification in urban surveillance videos. ICME 2016: 1--6
[2]
Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma: A Deep Learning-Based Approach to Progressive Vehicle Reidentification for Urban Surveillance. ECCV (2) 2016: 869--884
[3]
Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance. IEEE Trans. Multimedia 20(3): 645--658 (2018)
[4]
Aytac Kanaci, Xiatian Zhu, and Shaogang Gong. Vehicle reidentification in context. In Pattern Recognition - 40th German Conference, GCPR 2018, Stuttgart, Germany, September 10--12, 2018, Proceedings, 2018
[5]
P. Khorramshahi A. Kumar N. Peri S. S. Rambhatla J.-C. Chen and R. Chellappa. A dual-path model with adaptive attention for vehicle reidentification. In ICCV'19
[6]
Kumar, R., Weill, E., Aghdasi, F., & Sriram, P. (2020). A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding, Journal of Artificial Intelligence and Soft Computing Research, 10(1), 27--45.
[7]
Z. Tang M. Naphade S. Birchfield J. Tremblay W. Hodge R. Kumar S. Wang and X. Yang. Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In ICCV'19
[8]
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, and Yu-Chiang Frank Wang. Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). arXiv preprint arXiv: arXiv:1908.06052, 2020
[9]
Abhijit Suprem, Calton Pu. Looking GLAMO-Rous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention. Computer Vision and Pattern Recognition (cs.CV). arXiv preprint arXiv:2002.02256, 2020
Index Terms
- A Network Combining Local Features and Attention Mechanisms for Vehicle Re-Identification
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Published In
June 2020
250 pages
ISBN:9781450375511
DOI:10.1145/3430199
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Association for Computing Machinery
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Published: 21 December 2020
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AIPR 2020
AIPR 2020: 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
June 26 - 28, 2020
Xiamen, China
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View all- Rong LXu YZhou XHan LLi LPan X(2021)A vehicle re-identification framework based on the improved multi-branch feature fusion networkScientific Reports10.1038/s41598-021-99646-611:1Online publication date: 12-Oct-2021
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