The HDA+ Data Set for Research on Fully Automated Re-identification Systems

  • Dario Figueira
  • Matteo Taiana
  • Athira Nambiar
  • Jacinto Nascimento
  • Alexandre Bernardino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)

Abstract

There are no available datasets to evaluate integrated Pedestrian Detectors and Re-Identification systems, and the standard evaluation metric for Re-Identification (Cumulative Matching Characteristic curves) does not properly assess the errors that arise from integrating Pedestrian Detectors with Re-Identification (False Positives and Missed Detections). Real world Re-Identification systems require Pedestrian Detectors to be able to function automatically and the integration of Pedestrian Detector algorithms with Re-Identification produces errors that must be dealt with. We provide not only a dataset that allows for the evaluation of integrated Pedestrian Detector and Re-Identification systems but also sample Pedestrian Detection data and meaningful evaluation metrics and software, such as to make it “one-click easy” to test your own Re-Identification algorithm in an Integrated PD+REID system without having to implement a Pedestrian Detector algorithm yourself. We also provide body-part detection data on top of the manually labeled data and the Pedestrian Detection data, such as to make it trivial to extract your features from relevant local regions (actual body-parts). Finally we provide camera synchronization data to allow for the testing of inter-camera tracking algorithms. We expect this dataset and software to be widely used and boost research in integrated Pedestrian Detector and Re-Identification systems, bringing them closer to reality.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dario Figueira
    • 1
  • Matteo Taiana
    • 1
  • Athira Nambiar
    • 1
  • Jacinto Nascimento
    • 1
  • Alexandre Bernardino
    • 1
  1. 1.Institute for Systems and Robotics - LisbonLisbonPortugal

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