Cart (Loading....) | Create Account
Close category search window
 

A Structured Learning-Based Graph Matching Method for Tracking Dynamic Multiple Objects

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Hongkai Xiong ; Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China ; Dayu Zheng ; Qingxiang Zhu ; Botao Wang
more authors

Detecting multiple targets and obtaining a record of trajectories of identical targets that interact mutually infer countless applications in a large number of fields. However, it presents a significant challenge to the technology of object tracking. This paper describes a novel structured learning-based graph matching approach to track a variable number of interacting objects in complicated environments. Different from previous approaches, the proposed method takes full advantage of neighboring relationships as the edge feature in a structured graph, which performs better than using the node feature only. Therefore, a structured graph matching model is established, and the problem is regarded as structured node and edge matching between graphs generated from successive frames. In essence, it is formulated as the maximum weighted bipartite matching problem to be solved using the dynamic Hungarian algorithm, which is applicable to optimally solving the assignment problem in situations with changing edge costs or weights. In the proposed graph matching model, the parameters of the structured graph matching model are determined in a stochastic learning process. In order to improve the tracking performance, bilateral tracking is also used. Finally, extensive experimental results on Dynamic Cell, Football, and Car sequences demonstrate that the new approach effectively deals with complicated target interactions.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:23 ,  Issue: 3 )

Date of Publication:

March 2013

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.