Detecting Moving Objects and their Shadows: An Evaluation with the PETS2002 Dataset

This work presents a general-purpose method for moving visual object segmentation in videos and discusses results attained on sequences of PETS2002 datasets. The proposed approach, called Sakbot, exploits color and motion information to detect objects, shadows and ghosts, i.e. foreground objects with apparent motion. The method is based on background suppression in the color space. The main peculiarity of the approach is the exploitation of motion and shadow information to selectively update the background, improving the statistical background model with the knowledge of detected objects. The approach is able to detect Moving Visual Objects (MVOs), and stopped objects too, since the motion status is maintained at the level of tracking module. HSV color space is exploited for shadow detection in order to enhance both segmentation and background update. Time measures and precision performance analysis in tracking and counting people is provided for surveillance and monitoring purposes.