By Topic

Anomalous trajectory detection using the fusion of fuzzy rule and local regression analysis

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

3 Author(s)
Hanapiah, F. ; Centre for Multimodal Signal Process., Mimos Berhad, Kuala Lumpur, Malaysia ; Al-Obaidi, A.A. ; Chee Seng Chan

Motion trajectories provide rich spatio-temporal information about an object activity. In this paper, we present a novel anomaly detection framework to detect anomalous motion trajectory using the fusion of local regression analysis and fuzzy rule-based method. That is, first of all we address the problem by segmenting our moving objects using a Gaussian mixture background model. Secondly, visual tracking using probabilistic appearance manifolds to extract spatio-temporal trajectory. Thirdly, local regression analysis and data quantization are performed on the extracted trajectory such that the anomalous detection can be performed as the incoming data are acquired. Finally, through the accumulative rank of local regression analysis and a fuzzy rule-based anomaly detection framework to detect the anomalous trajectory. Experimental results on various challenging trajectory data has validated the effectiveness of the proposed method.

Published in:

Information Sciences Signal Processing and their Applications (ISSPA), 2010 10th International Conference on

Date of Conference:

10-13 May 2010