The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Similarity Learning with Spatial Constraints for Person Re-identification
Abstract
Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.
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Cites
Histograms of Oriented Gradients for Human Detection
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Large scale metric learning from equivalence constraints
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Person re-identification by symmetry-driven accumulation of local features
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Unsupervised Salience Learning for Person Re-identification
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Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
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Large Scale Online Learning of Image Similarity through Ranking
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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
A Discriminatively Learned CNN Embedding for Person Reidentification
Zheng, Z., Zheng, L., & Yang, Y. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3159171 · Cited by 874
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Liu, X., Zhao, H., Tian, M., Sheng, L., Shao, J., Yi, S., Yan, J., & Wang, X. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.46 · Cited by 567
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
End-to-End Comparative Attention Networks for Person Re-Identification
Liu, H., Feng, J., Qi, M., Jiang, J., & Yan, S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2700762 · Cited by 482
Metadata
- Authors
- Chen, D., Yuan, Z., Chen, B., & Zheng, N.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1268-1277
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2475284720
- Cited by
- 320
- References
- 33
- FWCI
- 29.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, D., Yuan, Z., Chen, B., & Zheng, N. (2016). Similarity Learning with Spatial Constraints for Person Re-identification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.142
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