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Multi-shot person re-identification based on appearance and spatial-temporal cues in a large camera network

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Abstract

Person re-identification is an important video analysis problem that aims to track people over non-overlapping views in a large camera network. The purpose is to find the same person from disjoint camera views at different times and locations. Most of the existing re-identification approaches assume pre-selected people bounding box captured by two non-overlapping cameras to perform the re-identification task while ignoring the inter-camera relationships in the network. In this paper, we propose a multi-shot person re-identification approach in a large camera network that relies on the appearance and spatial-temporal features. Firstly, we introduce a novel algorithm that selects a set of key appearance images depicting the different body postures from the target’s trajectory. Secondly, we model the appearance characteristics by extracting the multi-level semantic appearance representation descriptor that encodes both low- and mid-level appearance characteristics. Then, a dynamic gallery set is constructed containing the candidate people to whom the probe person can correspond based on his spatial-temporal characteristics. A novel camera network spatial-temporal graph is introduced to model the inter-camera relationships of the network in terms of entry/exit points, transition time, etc. The proposed approach has been experimentally validated on HDA+ and VIPeR datasets. The outcomes of this evaluation show promising results and demonstrate the effectiveness of our approach.

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Authors and Affiliations

  1. MIRACL-Faculty of Economics and Management, Sfax University, Sfax, Tunisia

    Mayssa Frikha

  2. MIRACL-Faculty of Sciences, Sfax University, Sfax, Tunisia

    Emna Fendri & Mohamed Hammami

Authors
  1. Mayssa Frikha
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  2. Emna Fendri
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  3. Mohamed Hammami
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Correspondence to Mayssa Frikha.

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Frikha, M., Fendri, E. & Hammami, M. Multi-shot person re-identification based on appearance and spatial-temporal cues in a large camera network. Machine Vision and Applications 32, 85 (2021). https://doi.org/10.1007/s00138-021-01213-6

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  • DOI: https://doi.org/10.1007/s00138-021-01213-6

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