Dealing with occlusion in a probabilistic object tracking method

Abstract

This paper presents an extension of a previously reported method for object tracking in video sequences to handle object occlusion. The new tracking method is embedded in a system that integrates recognition and tracking in a probabilistic framework. Our system uses object recognition results provided by a neural net that are computed from colour features of image regions for each frame. The location of tracked objects is represented through probability images that are updated dynamically using both recognition and tracking results. From these probabilities and a simple prediction of the apparent motion of the object in the image, a binary decision is made for each pixel and object. The new features of the proposed tracking method include the automated detection of occlusion and the adaptation of the motion prediction to the cases of entering occlusion, full occlusion and exiting occlusion. Experimental results show the effectiveness of the method except when the target object is occluded by an object with a similar appearance.