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  3. Chirla: Comprehensive High-resolution Identification And Re-identification For Large-scale Analysis.
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  3. Chirla: Comprehensive High-resolution Identification And Re-identification For Large-scale Analysis.

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

Bessie Dominguez-Dager1, Felix Escalona2, Francisco Gomez-Donoso2

  • 1Institute for Computing Research, P.O. Box 99, 03080, Alicante, Spain. bessie.dominguez@ua.es.

Scientific Data
|January 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces CHIRLA, a new dataset for long-term person re-identification (Re-ID) in real-world scenarios. It addresses challenges like clothing changes to improve Re-ID system robustness.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Person re-identification (Re-ID) is crucial for matching individuals across different cameras and times.
  • Existing Re-ID research often overlooks long-term variations due to clothing and physical changes, limiting real-world applicability.

Purpose of the Study:

  • To introduce CHIRLA (Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis), a novel dataset for video-based long-term person Re-ID.
  • To establish benchmark protocols for evaluating Re-ID algorithms in challenging, long-term scenarios.

Main Methods:

  • Collected over seven months in four indoor environments using seven cameras.
  • Captured data from 22 individuals with significant clothing and appearance variability.
  • Generated approximately 1 million bounding boxes with identity annotations via semi-automatic labeling.

    • Main Results:

    • CHIRLA dataset provides over five hours of video data.
    • The dataset includes challenging scenarios like occlusion and reappearance.
    • Benchmark protocols are defined for person tracking and Re-ID evaluation.

    Conclusions:

    • CHIRLA serves as a comprehensive benchmark to advance the development of robust Re-ID algorithms.
    • Facilitates the evaluation of Re-ID systems for reliable performance in long-term, real-world applications.
    • Publicly available benchmark code encourages further research and development.