A unified framework for automated person re-identification

  • Hong Quan Nguyen

    School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
    Viet-Hung Industry University, Hanoi, Vietnam
  • Thuy Binh Nguyen

    School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
    Faculty of Electrical-Electronic Engineering, University of Transport and Communications, Hanoi, VietNam
  • Duc Long Tran

    International Research Institute MICA, Hanoi University of Science and Technology, Hanoi, Vietnam
  • Thi Lan Le

    School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
    International Research Institute MICA, Hanoi University of Science and Technology, Hanoi, Vietnam
Email: thuybinh_ktdt@utc.edu.vn
Từ khóa: Person re-identification, human detection, tracking

Tóm tắt

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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