CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis

TL;DR

CHIRLA is a new dataset for improving long-term person re-identification in real-world settings with changing appearances.

Contribution

CHIRLA introduces a large-scale, high-resolution video dataset for long-term person Re-ID with realistic variations.

Findings

CHIRLA includes 22 individuals with over five hours of video and 1M annotated bounding boxes.

The dataset supports benchmarking in challenging scenarios like occlusion and multi-camera tracking.

Abstract

Person re-identification (Re-ID) is a key challenge in computer vision, requiring the matching of individuals across cameras, locations, and time. While most research focuses on short-term scenarios with minimal appearance changes, real-world applications demand robust systems that handle long-term variations caused by clothing and physical changes. We present CHIRLA, Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis, a novel dataset designed for video-based long-term person Re-ID. CHIRLA was recorded over seven months in four connected indoor environments using seven strategically placed cameras, capturing realistic movements with substantial clothing and appearance variability. The dataset includes 22 individuals, more than five hours of video, and about 1M bounding boxes with identity annotations obtained through semi-automatic labeling. We also define benchmark protocols for person tracking and Re-ID, covering diverse and challenging scenarios such as occlusion, reappearance, and multi-camera conditions. By introducing this comprehensive benchmark, we aim to facilitate the development and evaluation of Re-ID algorithms that can reliably perform in challenging, long-term real-world scenarios. The benchmark code is publicly available at: https://github.com/bdager/CHIRLA.

Figures