Multiple-Object Tracking in Large-Scale Scene

Wenbo YUAN  Zhiqiang CAO  Min TAN  Hongkai CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.7   pp.1903-1909
Publication Date: 2016/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7481
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
visual sensor network,  HOG,  improved particle filter,  re-identification,  object tracking,  

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In this paper, a multiple-object tracking approach in large-scale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.