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

• URL: http://arxiv.org/abs/2502.06681v1
• Date: Mon, 10 Feb 2025 17:07:43 GMT
• Title: CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
• Authors: Bessie Dominguez-Dager, Felix Escalona, Francisco Gomez-Donoso, Miguel Cazorla,
• Abstract summary: We present CHIRLA, Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis. CHIRLA consists of recordings from strategically placed cameras over a seven-month period, capturing significant variations in both temporal and appearance attributes. We collected more than five hours of video that we semi-automatically labeled to generate around one million bounding boxes with identity annotations.
• Score: 3.849237155005728
• License:
• 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.

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