New integrated framework for video based moving object tracking

Abstract

In this paper, we depict a novel approach to improve the moving object tracking system with particle filter using shape similarity and color histogram matching by a new integrated framework. The shape similarity between a template and estimated regions in the video sequences can be measured by their normalized cross-correlation of distance transformation image map. Observation model of the particle filter is based on shape from distance transformed edge features with concurrent effect of color information. The target object to be tracked forms the reference color window and its histogram are calculated, which is used to compute the histogram distance while performing a deterministic search for matching window. For both shape and color matching reference template window is created instantly by selecting any object in a video scene and updated in every frame. Experimental results have been offered to show the effectiveness of the proposed method. © 2009 Springer Berlin Heidelberg.