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

TL;DR

CHIRLA introduces a large-scale, long-term video dataset with challenging scenarios for person re-identification, aiming to advance research in real-world, long-term applications.

Contribution

This paper presents CHIRLA, a novel dataset and benchmark for long-term person Re-ID, addressing challenges like appearance changes and occlusion in real-world environments.

Findings

Dataset includes over 1 million bounding boxes with annotations.

Benchmark protocols cover diverse scenarios including occlusion and reappearance.

Publicly available code facilitates research and evaluation.

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.