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

Automatic Learning of Semantic Region Models for Event Recognition

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

4 Author(s)
Lei Gao ; Sch. of Comput. Sci. & Technol., Beihang Univ., Beijing ; Chao Li ; Yi Guo ; Zhang Xiong

The semantic structure of scene is important information used for interpretation of object behavior or event detection in video surveillance system. In this paper, we propose an automatic method for learning models of semantic region by analyzing the trajectories of moving objects in the scene. First, the trajectory is encoded to represent both the position of the object and its instantaneous velocity. Then, the hierarchical clustering algorithm is applied to cluster the trajectories according to different spatial and velocity distributions. In each cluster, trajectories are spatially close, have similar velocities of motion and represent one type of activity pattern. Based on the trajectory clusters, the statistical models of semantic region in the scene are generated by estimating the density and velocity distributions of each type of activity pattern. Finally, using the proposed semantic region models, anomalous activities are detected in two scenes. Experimental results demonstrate the effectiveness of the proposed method.

Published in:

Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on  (Volume:2 )

Date of Conference:

26-28 Nov. 2008

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.