Adaptive template based object tracking with particle filter

In this paper, we describe a new approach to improve the video based object tracking system with particle filter using shape similarity. It deals with single object tracking whose dynamics age highly non-linear. The shape similarity between a template and estimated regions in the video sequences can be measured by their normalized cross-correlation of distance transformation. Here within this present job, observation model of the particle filter is based on shape from distance transformed edge features. Template 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.