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VP-ReID: Vehicle and Person Re-Identification System

Published:05 June 2018Publication History

ABSTRACT

With the capability of locating and tracking specific suspects or vehicles in a large camera network, person Re-Identification (ReID) and vehicle ReID show potential to be a key technology in smart surveillance system. They have been drawing lots of attentions from both academia and industry. To demonstrate our recent research progresses on those two tasks, we develop a robust and efficient person and video ReID system named as VP-ReID. This system is build based on our recent works including Deep Convolutional Neural Network design for discriminative feature extraction, efficient off-line indexing, as well as distance metric optimization for deep feature learning. Constructed upon those algorithms, VP-ReID identifies query vehicle and person efficiently and accurately from a large gallery set.

References

  1. Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, and Bernt Schiele. 2016. Deepercut: A deeper, stronger, and faster multi-person pose estimation model ECCV.Google ScholarGoogle Scholar
  2. Jianing Li, Shiliang Zhang, Jingdong Wang, Wen Gao, and Qi Tian. 2017. LVreID: Person Re-Identification with Long Sequence Videos. arXiv preprint arXiv:1712.07286 (2017).Google ScholarGoogle Scholar
  3. Hongye Liu, Yonghong Tian, Yaowei Yang, Lu Pang, and Tiejun Huang. 2016. Deep relative distance learning: Tell the difference between similar vehicles CVPR.Google ScholarGoogle Scholar
  4. Xiaobin Liu, Shiliang Zhang, Qingming Huang, and Wen Gao. 2018. RAM: A Region-Aware Deep Model for Vehicle Re-Identification ICME.Google ScholarGoogle Scholar
  5. Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2017 a. Pose-driven Deep Convolutional Model for Person Re-identification ICCV.Google ScholarGoogle Scholar
  6. Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S Davis, and Wen Gao. 2017 b. Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017).Google ScholarGoogle Scholar
  7. Chi Su, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2018. Multi-type attributes driven multi-camera person re-identification. Pattern Recognition Vol. 75 (2018), 77--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chi Su, Shiliang Zhang, Fan Yang, Guangxiao Zhang, Qi Tian, Wen Gao, and Larry S Davis. 2017 c. Attributes driven tracklet-to-tracklet person re-identification using latent prototypes space mapping. Pattern Recognition Vol. 66 (2017), 4--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Yifan Sun, Liang Zheng, Weijian Deng, and Shengjin Wang. 2017. SVDNet for Pedestrian Retrieval. In ICCV.Google ScholarGoogle Scholar
  10. Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, and Xiaogang Wang. 2017. Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification. In ICCV.Google ScholarGoogle Scholar
  11. Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. 2017 a. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. arXiv preprint arXiv:1711.08565 (2017).Google ScholarGoogle Scholar
  12. Longhui Wei, Shiliang Zhang, Hantao Yao, Wen Gao, and Qi Tian. 2017 b. GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval ACM MM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hantao Yao, Shiliang Zhang, Dongming Zhang, Yongdong Zhang, Jintao Li, Yu Wang, and Qi Tian. 2017 b. Large-scale person re-identification as retrieval. In ICME.Google ScholarGoogle Scholar
  14. Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, and Qi Tian. 2017 a. One-Shot Fine-Grained Instance Retrieval. In ACM Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu. 2017. Deep Mutual Learning. arXiv preprint arXiv:1706.00384 (2017).Google ScholarGoogle Scholar
  16. Haiyu Zhao, Maoqing Tian, Shuyang Sun, Jing Shao, Junjie Yan, Shuai Yi, Xiaogang Wang, and Xiaoou Tang. 2017. Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In CVPR.Google ScholarGoogle Scholar
  17. Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In ICCV. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. Pedestrian Alignment Network for Large-scale Person Re-identification. arXiv preprint arXiv:1707.00408 (2017).Google ScholarGoogle Scholar

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  1. VP-ReID: Vehicle and Person Re-Identification System

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          cover image ACM Conferences
          ICMR '18: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval
          June 2018
          550 pages
          ISBN:9781450350464
          DOI:10.1145/3206025

          Copyright © 2018 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 5 June 2018

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          ICMR '18 Paper Acceptance Rate44of136submissions,32%Overall Acceptance Rate254of830submissions,31%

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