Paper Title:
Occluded Object Tracking Based on Mean Shift and Accumulation Error Suppression
  Abstract

To handle occlusion and accumulation error in tracking procedure, a novel method is proposed. Firstly, the object feature is modeled with kernel based color histogram. Then, mean shift is used to localizing the object with Kalman filter providing initial iteration location and scale. Object observation value is acquired by weighting the similarities of hue and saturation in x and y-directions. Finally, occlusion and scene disturbance are judged by maximal similarity and the matching deviation, so as to selectively update the object model. To suppress the accumulation error, the noise covariance is updated according to the iteration error in the latest N frames. Experimental results show that the proposed method is robust in tracking the occluded objects under complex scene.

  Info
Periodical
Chapter
Chapter 1: The Basic of Mechanics and Research Methods
Edited by
Huixuan Zhang, Ye Han, Fuxiao Chen and Jiuba Wen
Pages
20-24
DOI
10.4028/www.scientific.net/AMM.117-119.20
Citation
H. Lu, H. S. Li, S. M. Fei, W. F. Cao, "Occluded Object Tracking Based on Mean Shift and Accumulation Error Suppression", Applied Mechanics and Materials, Vols. 117-119, pp. 20-24, 2012
Online since
October 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Tie Qi Li, Zhang Ping Lu
Chapter 12: Computer-Aided Design, Manufacturing and Engineering
Abstract:Occlusion detection is a difficult traffic in the occlusion occurs, the existing system has been blocked based on the regional and...
4348
Authors: M.M. Naushad Ali, M. Abdullah-Al-Wadud, Seok Lyong Lee
Chapter 8: Voice, Image and Video Processing
Abstract:Moving human detection and tracking are challenging tasks in computer vision. Human motion is usually non-linear and non-Gaussian, and thus...
1200