Hits:

Indexed by:期刊论文

Date of Publication:2012-01-01

Journal:International Journal of Digital Content Technology and its Applications

Included Journals:Scopus

Volume:6

Issue:22

Page Number:467-474

ISSN No.:19759339

Abstract:In this paper, we propose a visual object tracking algorithm based on maximum likelihood estimation (MLE) with the sparsity constraint. The model of sparsity constrained MLE is established. Abnormal pixels in the samples will be assigned with low weights to reduce their affects on the tracking algorithm. The object tracking results is obtained by using Bayesian MAP(maximum a posteriori probability) estimation. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes(e.g. occlusion,rotation,scale change, illumination, etc.).