Introducing scene understanding to person re-identification using a spatio-temporal multi-camera model

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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.
Original languageEnglish
Publication statusPublished - 2020
EventIS&T International Symposium on Electronic Imaging 2020, Image Processing: Algorithms and Systems XVIII - Burlingame, United States
Duration: 26 Jan 202030 Jan 2020
Conference number: XVIII

Conference

ConferenceIS&T International Symposium on Electronic Imaging 2020, Image Processing: Algorithms and Systems XVIII
Abbreviated titleIPAS2020
CountryUnited States
CityBurlingame
Period26/01/2030/01/20

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