Kernel based robust object tracking using model updates and Gaussian pyramids

Vision based tracking, being a challenging engineering problem is one of the hot research areas in machine vision. In recent studies Kernel based tracking using Bhattacharya similarity measure is shown to be an efficient technique for non-rigid object tracking through the sequence of images. In this paper we presented a robust and efficient tracking approach for targets having larger motions as compared to their sizes. Our tracking approach is based on calculating the Gaussian pyramids of the images and then applying mean shift algorithm at each pyramid level for tracking the target. Model based tracking often suffers abrupt changes in target model, which is compensated by the model updates of target. This leads to a very efficient arid robust nonparametric tracking algorithm The new method is easily able to track the fast moving targets and is more robust and environment independent as compared to original kernel based object tracking.