Cross-view Identical Part Area Alignment for Person Re-identification
| Autor: | Zheng Wang, Jun Chen, Xu Dongshu, Chao Liang, Ruimin Hu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Computer science business.industry 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision 02 engineering and technology Artificial intelligence Human body Invariant (mathematics) business Re identification |
| Zdroj: | ICASSP |
| Popis: | Person re-identification aims to associate images captured by non-overlapping cameras. It is a challenging task because images are often in different conditions such as background clutter, illumination variation, viewpoint changes and different camera settings. Viewpoint changes and pose variations often cause body part self-occlusion and misalignment. To deal with the problem, local features from human body parts are extracted. However, with viewpoint changes, the body parts also rotate horizontally. It is inappropriate to extract feature from entire area of body parts directly because the visible surface of body parts would turn away if viewpoint changes. Comparing identical areas provides a new way to pay attention to the details of person images. In this paper, we propose a Rotation Invariant Network to find the identical areas in cross-view images to extract robust local features. Extensive experiment show the effectiveness of our method on public datasets including CUHK03, Market1501 and DukeMTMC. |
| Databáze: | OpenAIRE |
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