The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Joint Detection and Identification Feature Learning for Person Search
Open accessAbstract
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks-pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.
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ImageNet Large Scale Visual Recognition Challenge
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Cited by
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861
Self-citationTowards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195
Person Re-identification in the Wild
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.357 · Cited by 830
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification
Zhong, Z., Zheng, L., Luo, Z., Li, S., & Yang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00069 · Cited by 685
Self-citation
Metadata
- Authors
- Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3376-3385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963574614
- Cited by
- 890
- References
- 46
- FWCI
- 27.59
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X. (2017). Joint Detection and Identification Feature Learning for Person Search. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.360
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