Paper Title
Object Tracking Based On Markov Chain And Conditional Probability

The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, here propose a method to track the object of interest. Using this new object tracking method can reduce the computation time and also it is very useful for high definition videos. Using this new method it is possible to trace the object even if it is moving or changes to stands still from movement. Another important attraction of this paper is automatic fixation. All these features that are computation time reduction and automatic fixation are done using k-means clustering, markov chain and conditional probability. Each cluster mean value and centroid point value is updated in to next frame. Computation time reduction is achieving with the help of k means clustering and automatic fixation is done using markov chain concept and conditional probability equation for Bayesian estimator. This is an efficient object tracking method.