Paper Info
Title
Tracking multiple interacting targets in a camera network
Abstract
We propose a tracker for multiple interacting targets in a camera network.A model is developed to decide the group state of each trajectory.The tracking problem is converted to a network flow problem. In this paper we propose a framework for tracking multiple interacting targets in a wide-area camera network consisting of both overlapping and non-overlapping cameras. Our method is motivated from observations that both individuals and groups of targets interact with each other in natural scenes. We associate each raw target trajectory (i.e., a tracklet) with a group state, which indicates if the trajectory belongs to an individual or a group. Structural Support Vector Machine (SSVM) is applied to the group states to decide if merge or split events occur in the scene. Information fusion between multiple overlapping cameras is handled using a homography-based voting scheme. The problem of tracking multiple interacting targets is then converted to a network flow problem, for which the solution can be obtained by the K-shortest paths algorithm. We demonstrate the effectiveness of the proposed algorithm on the challenging VideoWeb dataset in which a large amount of multi-person interaction activities are present. Comparative analysis with state-of-the-art methods is also shown.
Year
DOI
Venue
2015
10.1016/j.cviu.2015.01.002
Computer Vision and Image Understanding
Keywords
Field
DocType
multi-target tracking,multi-camera tracking,network flow,wide-area camera network,interacting targets
Flow network,Computer vision,Multi target tracking,Support vector machine,Camera network,Homography,Artificial intelligence,Merge (version control),Information fusion,Trajectory,Mathematics
Journal
Volume
Issue
ISSN
134
C
1077-3142
Citations 
PageRank 
References 
8
0.49
43
Authors
3
Name
Order
Citations
PageRank
Shu Zhang1383.32
Yingying Zhu241026.41
Amit K. Roy Chowdhury3115373.96