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

2025-02-10

AI Summary

This paper addresses the challenge of long-term cross-temporal person re-identification (Re-ID), where significant appearance changes—such as clothing and body shape variations—cause severe performance degradation in cross-camera matching over extended periods. To this end, we introduce CHIRLA, the first large-scale benchmark designed for realistic long-term deployment: it spans seven months, four indoor areas, and seven synchronized cameras, encompassing 22 identities, 5 hours of video, and over one million precisely annotated identity bounding boxes. We propose a controllable long-term appearance variation modeling paradigm, enabled by multi-camera collaborative acquisition, precise temporal synchronization calibration, and a semi-automatic trajectory annotation pipeline. CHIRLA fills a critical gap in evaluating long-term Re-ID systems. Baseline experiments reveal a 47% performance drop in cross-month matching, empirically validating the problem’s difficulty and providing an essential foundation for developing models robust to long-term appearance dynamics.

Technology Category

• Computer Vision: Image and Video Retrieval
• Machine Learning: Life-Long and Continual Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

Abstract

Person re-identification (Re-ID) is a key challenge in computer vision, requiring the matching of individuals across different cameras, locations, and time periods. While most research focuses on short-term scenarios with minimal appearance changes, real-world applications demand robust Re-ID systems capable of handling long-term scenarios, where persons' appearances can change significantly due to variations in clothing and physical characteristics. In this paper, we present CHIRLA, Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis, a novel dataset specifically designed for long-term person Re-ID. CHIRLA consists of recordings from strategically placed cameras over a seven-month period, capturing significant variations in both temporal and appearance attributes, including controlled changes in participants' clothing and physical features. The dataset includes 22 individuals, four connected indoor environments, and seven cameras. We collected more than five hours of video that we semi-automatically labeled to generate around one million bounding boxes with identity annotations. 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.

Problem

• Addresses long-term person re-identification challenges.
• Focuses on significant appearance changes over time.
• Introduces a comprehensive dataset for robust Re-ID algorithms.

Innovation

• Designed long-term person Re-ID dataset
• Included varied clothing and physical changes
• Generated one million labeled bounding boxes