The accurate locating for the target is critical for robust visual tracking methods. This paper addresses the target position confirmation and relocation in mean shift tracking, and proposes a novel method to integrate a MOSSE based correlation filter into the mean shift tracker to obtain its ability of accurate locating. To confirm whether the estimated location of the target is accurate, four measures are evaluated. If the proposed conditions for relocating the target are satisfied, the estimated target position will be adjusted to be more accurate. When the target is occluded, a relocating approach is developed using the correlation filter to find the target after occlusion. The target model and the filter template are updated in each frame according to the evaluation results of the estimated target. Experimental results show the integration of the correlation filter can help the mean shift tracker locate and relocate the target well.
Corner detection has been shown to be very useful in many computer vision applications. Some valid approaches have been proposed, but few of them are accurate, efficient and suitable for complex applications (such as DSP). In this paper, a corner detector using invariant analysis is proposed. The new detector assumes an ideal corner of a gray level image should have a good corner structure which has an annulus mask. An invariant function was put forward, and the value of which for the ideal corner is a constant value. Then, we could verify the candidate corners by compare their invariant function value with the constant value. Experiments have shown that the new corner detector is accurate and efficient and could be used in some complex applications because of its simple calculation.