Paper Info
Title
Adaptive Person Re-Identification Based On Visible Salient Body Parts In Large Camera Network
Abstract
Person re-identification consists in recognizing the same person across non-overlapping camera views at different times and locations. It presents an important yet challenging task for intelligent video surveillance systems due to the large variations of pose, viewpoint, lighting and occlusion between the different camera views. Although a variety of algorithms have been presented in the past few years, most of them usually assume a pre-cropped bounding box of fully visible people taken from two different cameras to perform the re-identification process. However, in real-world video surveillance systems several challenges need to be addressed such as re-identifying truncated people and alleviating the hash lighting variation of the different camera views. In this paper, we propose an adaptive person re-identification approach that re-identifies a person irrespective of his status, i.e. truncated or fully visible, based on the apparent salient body regions driven by their robust appearance characteristics in a large camera network. The proposed approach has been experimentally validated on the High Definition Analytics (HDA) and viewpoint invariant perdestrian recognition data sets which include several re-identification challenges. The outcomes of this evaluation show promising results and demonstrate the effectiveness of our approach compared to other state-of-the-art approaches.
Year
DOI
Venue
2017
10.1093/comjnl/bxx004
COMPUTER JOURNAL
Keywords
Field
DocType
videosurveillance, person re-identification, truncated people, head-shoulder detection, appearance-based features
Computer vision,Computer graphics (images),Computer science,Camera network,Artificial intelligence,Salient
Journal
Volume
Issue
ISSN
60
11
0010-4620
Citations 
PageRank 
References 
0
0.34
12
Authors
3
Name
Order
Citations
PageRank
Emna Fendri1127.28
Mayssa Frikha242.43
Mohamed Hammami318130.54