Journals Proceedings

Abstract

The object tracking algorithms based on men shift are good and efficient. But they have limitations like inaccuracy of target localization and sometimes complete tracking failure. These difficulties arises because of the fact that in basic kernel based mean shift tracking algorithm, the centroid is not always at the center of the target and the size of tracking window remains constant even if there is a major change in the size of object. It causes introduction of large number of background pixels in the object model which give localization errors or complete tracking failure. One more limitation of basic mean shift tracking algorithm is that it does not have an adaptive stop threshold in searching the target procedure. So even at times it gives proper target localization the computation time is much more. To deal with these challenges a new robust mean shift object tracking algorithm with improved tracking velocity and least localization errors is proposed in this paper. This approach includes relocation of the track window on the middle of the target object in every frame using edge based centroid calculation technique and automatic size adjustment of tracking window so that minimum background pixels will be introduced in object model. Also computational speed is improved by limiting the MS iterations count. The proposed algorithm show good results for almost all the tracking challenges faced by basic mean shift kernel tracking method.