A novel visual tracking method based on lazy interaction and irregular patches is proposed

to improving the accuracy and success rate of target tracking

especially on the challenges of background clutters and illumination variation. First

it divides the target into a lot of irregular target patches by using a simple and lazy interactive process

and each target patches is initialized by the kernel correlation filtering. Then

each target patches is tracked based on the kernel correlation filtering. At the same time

the proposed method updates the patches model in a simple way to satisfy the constant changes of the target and the environment. When a simple update fails to deal with the target transformation

the lazy interactive process resamples the related patches and builds a more accurate target patch model. Finally

according to the position of all the target patches on the new frame

the target position is computed by the hough-voting scheme. The main innovation is that sampling the target patches through lazy interaction ensures the effectiveness of describing target patches. And by sampling the irregular patches

it effectively describes the target characteristics. Using the 27 videos of visual tracking benchmark

the quantitative evaluation of different trackers is achieved. Many experiments demonstrate that the proposed method perform is better in dealing with target illumination variation

rotation

background clutters and so on.