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Volume: 32 | Article ID: art00010
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Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model
  DOI :  10.2352/ISSN.2470-1173.2020.10.IPAS-095  Published OnlineJanuary 2020
Abstract

In this paper, we investigate person re-identification (re-ID) in a multi-camera network for surveillance applications. To this end, we create a Spatio-Temporal Multi-Camera model (ST-MC model), which exploits statistical data on a person’s entry/exit points in the multi-camera network, to predict in which camera view a person will re-appear. The created ST-MC model is used as a novel extension to the Multiple Granularity Network (MGN) [1], which is the current state of the art in person re-ID. Compared to existing approaches that are solely based on Convolutional Neural Networks (CNNs), our approach helps to improve the re-ID performance by considering not only appearance-based features of a person from a CNN, but also contextual information. The latter serves as scene understanding information complimentary to person re-ID. Experimental results show that for the DukeMTMC-reID dataset [2][3], introduction of our ST-MC model substantially increases the mean Average Precision (mAP) and Rank-1 score from 77.2% to 84.1%, and from 88.6% to 96.2%, respectively.

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Xin Liu, Herman G.J. Groot, Egor Bondarev, Peter H.N. de With, "Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Modelin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVIII,  2020,  pp 95-1 - 95-13,  https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-095

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