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Tracklet style transfer and part-level feature description for person reidentification in a camera network

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Abstract

Reidentifying multiple objects in a camera network are a difficult problem, especially when determining whether the same object appears in a different place at a different time, captured by another camera. In this paper, we propose a novel tracklet-based approach for reidentifying objects despite the illumination condition differences that occur at various times of day. A similarity search is performed by comparing part-level object feature descriptions. Tracking in each camera is made by a recurrent neural network, and the matching between cameras is done by using a similarity neural network to obtain an output in the form of a similarity score. Our approach consists of two main phases. In the first phase, preprocessing is performed through the transfer of tracklets from several cameras. This process generates more samples from each camera, which is beneficial for training. In the second phase, the object definition is applied, which considers appearance information, temporal information and the similarity calculation, hence making object reidentification easier. We have analyzed the proposed strategy when applied to pedestrian reidentification databases in comparison with state-of-the-art work to prove its robustness.

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Authors and Affiliations

  1. Universite Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, CLERMONT-FERRAND, F-63000, France

    Yosra Dorai & Frederic Chausse

  2. Université de Sousse, École Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia

    Yosra Dorai, Sami Gazzah & Najoua Essoukri Ben Amara

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  1. Yosra Dorai
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  2. Sami Gazzah
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  3. Frederic Chausse
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  4. Najoua Essoukri Ben Amara
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Correspondence to Yosra Dorai.

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Dorai, Y., Gazzah, S., Chausse, F. et al. Tracklet style transfer and part-level feature description for person reidentification in a camera network. Pattern Anal Applic 24, 875–886 (2021). https://doi.org/10.1007/s10044-021-00990-0

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