The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Partial Person Re-Identification
Abstract
We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views. This differs significantly from the conventional person re-id setting where it is assumed that the full body of a person is detected and aligned. To solve this more challenging and realistic re-id problem without the implicit assumption of manual body-parts alignment, we propose a matching framework consisting of 1) a local patch-level matching model based on a novel sparse representation classification formulation with explicit patch ambiguity modelling, and 2) a global part-based matching model providing complementary spatial layout information. Our framework is evaluated on a new partial person re-id dataset as well as two existing datasets modified to include partial person images. The results show that the proposed method outperforms significantly existing re-id methods as well as other partial visual matching methods.
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Citations
Cites
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100
Cited by
Harmonious Attention Network for Person Re-identification
Li, W., Zhu, X., & Gong, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00243 · Cited by 1,449
Self-citationJoint Detection and Identification Feature Learning for Person Search
Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.360 · Cited by 890
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
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., & Sun, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00648 · Cited by 512
Attention-Aware Compositional Network for Person Re-identification
Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00226 · Cited by 502
Metadata
- Authors
- Zheng, W.-S., Li, X., Xiang, T., Liao, S., Lai, J., & Gong, S.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 4678-4686
- OpenAlex
- W2235916736
- Cited by
- 295
- References
- 1
- FWCI
- 7.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zheng, W.-S., Li, X., Xiang, T., Liao, S., Lai, J., & Gong, S. (2015). Partial Person Re-Identification. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.531
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