Summary of Enhancing Long-term Person Re-identification Using Global, Local Body Part, and Head Streams, by Duy Tran Thanh and Yeejin Lee and Byeongkeun Kang
Enhancing Long-Term Person Re-Identification Using Global, Local Body Part, and Head Streams
by Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for long-term person re-identification is proposed to overcome the limitations of current methods, which assume people do not change their clothes. The framework consists of three streams: global, local body part, and head streams. These streams encode identity-relevant information from entire images, cropped images of the head region, and individual body parts, respectively. To train the framework, a weighted summation of three losses is used: identity classification loss, pair-based loss, and pseudo body part segmentation loss. The proposed method outperforms previous state-of-the-art methods on three publicly available datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to recognize people even when they wear different clothes or accessories is developed in this research. Currently, most person re-identification systems only work well for short periods of time because they don’t account for changes in clothing. To overcome this limitation, the authors create a new framework that considers both situations where people change their clothes and those where they stay consistent. The framework uses information from whole images, just the head, and specific body parts to identify individuals. This approach is shown to be more effective than previous methods on several well-known datasets. |
Keywords
» Artificial intelligence » Classification
