The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Occluded Person Re-Identification
Open accessAbstract
Person re-identification (re-id) suffers from a serious occlusion problem when applied to crowded public places. In this paper, we propose to retrieve a full-body person image by using a person image with occlusions. This differs significantly from the conventional person re-id problem where it is assumed that person images are detected without any occlusion. We thus call this new problem the occluded person re-identitification. To address this new problem, we propose a novel Attention Framework of Person Body (AFPB) based on deep learning, consisting of 1) an Occlusion Simulator (OS) which automatically generates artificial occlusions for full-body person images, and 2) multi-task losses that force the neural network not only to discriminate a person's identity but also to determine whether a sample is from the occluded data distribution or the full-body data distribution. Experiments on a new occluded person re-id dataset and three existing benchmarks modified to include full-body person images and occluded person images show the superiority of the proposed method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_2 · Cited by 2,914
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
Salient Object Detection: A Benchmark
Borji, A., Cheng, M.-M., Jiang, H., & Li, J. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2487833 · Cited by 1,364
An improved deep learning architecture for person re-identification
Ahmed, E., Jones, M., & Marks, T. K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299016 · Cited by 1,248
MARS: A Video Benchmark for Large-Scale Person Re-Identification
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_52 · Cited by 965
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
SVDNet for Pedestrian Retrieval
Sun, Y., Zheng, L., Deng, W., & Wang, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.410 · Cited by 825
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587581 · Cited by 721
Cited by
(2014) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-1-4471-6296-4 · Cited by 365
Solving Inefficiency of Self-supervised Representation Learning
Wang, G., Wang, K., Wang, G., Torr, P. H. S., & Lin, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00937 · Cited by 65
Self-citationContent-Adaptive Auto-Occlusion Network for Occluded Person Re-Identification
Zhao, C., Qu, Z., Jiang, X., Tu, Y., & Bai, X. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3290525 · Cited by 50
Metadata
- Authors
- Zhuo, J., Chen, Z., Lai, J., & Wang, G.
- Venue
- 2018 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-6
- OpenAlex
- W2964201641
- Cited by
- 282
- References
- 18
- FWCI
- 5.23
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Occluded Person Re-Identification. https://app.cochat.ai/papers/10.1109/icme.2018.8486568
- The paper
- Zhuo, J., Chen, Z., Lai, J., & Wang, G. (2018). Occluded Person Re-Identification. 2018 IEEE International Conference on Multimedia and Expo (ICME). https://doi.org/10.1109/icme.2018.8486568
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
