@inproceedings{19a426532a5743d49da5d5064b6a50ea,
title = "Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model",
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{\textquoteright}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.",
keywords = "CNN, Context information DukeMTMC, DukeMTMC-reID, Person re-identification, Scene understanding, Spatial constraints, Temporal constraints",
author = "Xin Liu and Groot, {Herman G.J.} and Egor Bondarev and {de With}, {Peter H.N.}",
year = "2020",
month = jan,
day = "26",
doi = "10.2352/ISSN.2470-1173.2020.10.IPAS-095",
language = "English",
series = "Electronic Imaging",
publisher = "Society for Imaging Science and Technology (IS&T)",
booktitle = "Proceedings IS&T International Symposium on Electronic Imaging",
address = "United States",
note = "18th Image Processing: Algorithms and Systems Conference, IPAS 2020 ; Conference date: 26-01-2020 Through 30-01-2020",
}