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

TL;DR

The paper introduces ENTIRe-ID, a large and diverse dataset with over 4.45 million images from 37 cameras, aimed at improving person re-identification by addressing domain variability and model generalization challenges.

Contribution

It provides a new extensive and diverse dataset for person re-identification, covering various real-world scenarios to enhance model robustness and generalization.

Findings

Dataset covers diverse environments and conditions.

Enables training of more robust ReID models.

Supports research on domain variability.

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