Improving Integrality of Detected Moving Objects Based on Image Matting

The integrality of moving objects is the basis for video-based object tracking and action analysis. But it often becomes unreliable as a result of shadow elimination when the object has similar properties with real shadow. The proposed methods manage to improve the integrality of detected moving objects as much as possible, by image matting operated on candidate shadow regions. Existing approaches for image matting require manual labeling of foreground and background. Considering the moving feature points in shadow may cause classification errors, we propose automatic scribbling methods based on Scale invariant feature transform (SIFT) and Speeded-up robust feature (SURF) respectively. Experiments demonstrate that our methods eliminate real shadow effectively and improve object segmentation in the case of object parts and shadows presenting similar characteristics.