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) , 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 , 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.
|Publication status||Published - 2020|
|Event||IS&T International Symposium on Electronic Imaging 2020, Image Processing: Algorithms and Systems XVIII - Burlingame, United States|
Duration: 26 Jan 2020 → 30 Jan 2020
Conference number: XVIII
|Conference||IS&T International Symposium on Electronic Imaging 2020, Image Processing: Algorithms and Systems XVIII|
|Period||26/01/20 → 30/01/20|