Title: Visual object tracking using Gaussian process and sparse representation

Authors: Samira Ghareh Gozlou; Morteza Ghareh Gozlou

Addresses: Department of Computer Engineering and Information Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran ' Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK

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.

Keywords: object tracking; Gaussian process; sparse representation; binary classification; variational inference; video sequences; target motion; probabilistic modelling; object location.

DOI: 10.1504/IJAPR.2015.069541

International Journal of Applied Pattern Recognition, 2015 Vol.2 No.2, pp.128 - 141

Received: 25 Jul 2014
Accepted: 28 Aug 2014

Published online: 22 May 2015 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article