Moving object contour detection based on S-T characteristics in surveillance

We present a method for moving object contours detection based on spatial-temporal characteristics. Using S-T features, the contour of moving object can be well distinguished from background; therefore the moving objects are detected without the need of establishing and updating background models. ne detection method can handle situations where the background of the scene suffers from the noises due to the various facts, including the weather condition such as snow or fog and flicker of leafs on trees, and bushes. The algorithm estimates the probability of observing pixel as a contour pixel based on a sample of intensity values for each pixel during a period of time and its local gradient in current frame. The experiments show that this method is sensitive to changes caused by moving objects and is able to avoid the affection of complex background. The paper also shows how to separate multi-person based on the contour detection results using template matching. The approach runs in real-time and achieves sensitive detection.