点击次数：

论文类型：期刊论文

发表时间：2012-01-01

发表刊物：International Journal of Digital Content Technology and its Applications

收录刊物：Scopus

卷号：6

期号：22

页面范围：467-474

ISSN号：19759339

摘要：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.).