In visual tracking

how to construct an object model to cope with the appearance change is one of the key problems to improve tracking precision and stability.To resolve this problem

this paper proposes to construct a local patch model using the initial labels of the pixels in tracking area

and proposes an online visual tracking algorithm based on local patch learning under the framework of Bayesian theory.The detailed operation is as follows.Firstly

it constructs the local patch model according to the initialized tracking area.Then

it utilizes the object model to learn the local patches in current tracking area and estimates the current state via Bayes estimation.Finally

it updates the local patch model by feature clustering.The experiment results indicate that the proposed algorithm obtains a distinct improvement in coping with appearance change

and exceeds the recent local patch-based trackers in both tracking precision and stability.