Automated human tracking using advanced mean shift algorithm

For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this paper proposes a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules uses human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helps to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling will help to initiate the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent.