By Topic

Motion constraint Markov network model for multi-target tracking

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
$33 $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

2 Author(s)
Mingjun Wu ; Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China ; Xianrong Peng

The typical Markov network for modeling interaction among targets can handle error merge problem, but it suffers from labeling problem due to the blind competition among collaborative trackers. In this paper, we propose a motion constraint Markov network model for multiple target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior, this proposed model can overcome the blind competition for image resources and direct the label to the corresponding target even in the case of severe occlusion. In addition, the motion constraint prior is formulated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Finally, this model is inferred within the framework of variational mean field method. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.

Published in:

Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on

Date of Conference:

7-9 July 2008