The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Learning to rank in person re-identification with metric ensembles
Abstract
We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-1 recognition rates from 40% to 50% on the iLIDS benchmark, 16% to 18% on the PRID2011 benchmark, 43% to 46% on the VIPeR benchmark, 34% to 53% on the CUHK01 benchmark and 21% to 62% on the CUHK03 benchmark.
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Cites
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DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Information-theoretic metric learning
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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
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.149 · Cited by 1,247
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Xiao, T., Li, H., Ouyang, W., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.140 · Cited by 958
Joint 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
Learning a Discriminative Null Space for Person Re-identification
Zhang, L., Xiang, T., & Gong, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.139 · Cited by 621
Metadata
- Authors
- Paisitkriangkrai, S., Shen, C., & van den Hengel, A.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1846-1855
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1920259731
- Cited by
- 385
- References
- 43
- FWCI
- 29.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298794
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