Visual object tracking using Gaussian process and sparse representation Online publication date: Fri, 22-May-2015
by Samira Ghareh Gozlou; Morteza Ghareh Gozlou
International Journal of Applied Pattern Recognition (IJAPR), Vol. 2, No. 2, 2015
Abstract: In this paper, we present a new method to solve the object tracking problem in video sequences based on the combination of sparse representation and Gaussian process. Most of sparse representation based trackers only consider the holistic representation and do not make full use of motion information of the target, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust probabilistic tracking model in which the motion information of the target object in the previous frames (this information is captured by Gaussian process) is used to define a prior distribution on the object location in the current frame. Then, by using an appropriate likelihood distribution model (this is done via sparse representation), we can compute the posterior distribution of the object location on the current frame. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed object tracking algorithm.
Online publication date: Fri, 22-May-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Pattern Recognition (IJAPR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com