Summary of Entire-id: An Extensive and Diverse Dataset For Person Re-identification, by Serdar Yildiz et al.
ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification
by Serdar Yildiz, Ahmet Nezih Kasim
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed ENTIRe-ID dataset tackles the challenges of domain variability and model generalization in person reidentification, a crucial computer vision task. The dataset comprises over 4.45 million images from 37 different cameras in varied environments, ensuring a realistic training platform for ReID models. This comprehensive collection addresses existing limitations by covering diverse lighting conditions, angles of view, and human activities. The ENTIRe-ID dataset is publicly available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ENTIRe-ID dataset helps computers better recognize people in different settings. It has many images from 37 cameras in various places, making it a great training tool for person reidentification models. This means the model can learn to work well in real-world scenarios with changing lighting and angles. |
Keywords
» Artificial intelligence » Generalization
