ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification

• URL: http://arxiv.org/abs/2405.20465v1
• Date: Thu, 30 May 2024 20:26:47 GMT
• Title: ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification
• Authors: Serdar Yildiz, Ahmet Nezih Kasim,
• Abstract summary: ENTIRe-ID dataset comprises over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of domain variability and model generalization. This design ensures a realistic and robust training platform for ReID models.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing importance of person reidentification in computer vision has highlighted the need for more extensive and diverse datasets. In response, we introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of domain variability and model generalization, areas where existing datasets for person re-identification have fallen short. The ENTIRe-ID dataset stands out for its coverage of a wide array of real-world scenarios, encompassing various lighting conditions, angles of view, and diverse human activities. This design ensures a realistic and robust training platform for ReID models. The ENTIRe-ID dataset is publicly available at https://serdaryildiz.github.io/ENTIRe-ID

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