Paper Info
Title
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets.
Abstract
In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications.
Year
Venue
Field
2017
arXiv: Computer Vision and Pattern Recognition
Computer vision,Graph,Multi target tracking,Parametrization,Computer science,Artificial intelligence,Quadratic programming,Evolutionary game theory,Cluster analysis,Merge (version control),Detector
DocType
Volume
Citations 
Journal
abs/1706.06196
10
PageRank 
References 
Authors
0.55
28
5
Name
Order
Citations
PageRank
yonatan tariku tesfaye1141.30
Eyasu Zemene2100.55
A. Prati3123880.43
Marcello Pelillo41888150.33
Mubarak Shah516522943.74