By Topic

Object recognition and tracking for remote video surveillance

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

1 Author(s)
Foresti, G.L. ; Dipt. di Matematica e Inf., Udine Univ., Italy

A system for real-time object recognition and tracking for remote video surveillance is presented. In order to meet real-time requirements, a unique feature, i.e., the statistical morphological skeleton, which achieves low computational complexity, accuracy of localization, and noise robustness has been considered for both object recognition and tracking. Recognition is obtained by comparing an analytical approximation of the skeleton function extracted from the analyzed image with that obtained from model objects stored into a database. Tracking is performed by applying an extended Kalman filter to a set of observable quantities derived from the detected skeleton and other geometric characteristics of the moving object. Several experiments are shown to illustrate the validity of the proposed method and to demonstrate its usefulness in video-based applications

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:9 ,  Issue: 7 )