Shadow resistant tracking using inertia constraints

Abstract

In this paper, we present a new method for tracking objects with shadows. Traditional motion-based tracking schemes cannot usually distinguish the shadow from the object itself, and this results in a falsely captured object shape. If we want to utilize the object's shape information for a pattern recognition task, this poses a severe difficulty. In this paper we present a color processing scheme to project the image into an illumination invariant space such that the shadow's effect is greatly attenuated. The optical flow in this projected image together with the original image is used as a reference for object tracking so that we can extract the real object shape in the tracking process. We present a modified snake model for general video object tracking. Two new external forces are introduced into the snake equation based on the predictive contour and a new chordal string shape descriptor such that the active contour is attracted to a shape similar to the one in the previous video frame. The proposed method can deal with the problem of an object's ceasing movement temporarily, and can also avoid the problem of the snake tracking into the object interior. Global affine motion estimation is applied to mitigate the effect of camera motion, and hence the method can be applied in a general video environment. Experimental results show that the proposed method can track the real object even if there is strong shadow influence.