The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Open accessAbstract
The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID systems are required to cope with the massive visual data being produced by video surveillance systems. Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively. GLAD explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to eliminate the huge redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results show GLAD achieves competitive accuracy compared to the state-of-the-art methods. Our retrieval framework significantly accelerates the online Re-ID procedure without loss of accuracy. Therefore, this work has potential to work better on person Re-ID tasks in real scenarios.
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Cites
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Cited by
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00016 · Cited by 2,118
Self-citationBag of Tricks and a Strong Baseline for Deep Person Re-Identification
Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00190 · Cited by 1,584
TransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244
A Strong Baseline and Batch Normalization Neck for Deep Person Re-Identification
Luo, H., Jiang, W., Gu, Y., Liu, F., Liao, X., Lai, S., & Gu, J. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2958756 · Cited by 593
ABD-Net: Attentive but Diverse Person Re-Identification
Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., Ren, Z., & Wang, Z. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00844 · Cited by 561
Metadata
- Authors
- Wei, L., Zhang, S., Yao, H., Gao, W., & Tian, Q.
- Venue
- Proceedings of the 25th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2017-10-19
- Pages
- 420-428
- OpenAlex
- W2755066373
- Cited by
- 409
- References
- 42
- FWCI
- 19.09
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wei, L., Zhang, S., Yao, H., Gao, W., & Tian, Q. (2017). GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval. Proceedings of the 25th ACM international conference on Multimedia. https://doi.org/10.1145/3123266.3123279
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