Tracking by using dynamic shape model learning in the presence of occlusion

Abstract

The paper presents a new corner-model based learning method able to track non-rigid objects in the presence of occlusion. A voting mechanism followed by a probability density analysis of the voting space histogram is used to estimate new position of the target. The model is updated at any frame. The problem rises in the occlusion events where the occluder corners affect the model and the tracker may follow the occluder. The key point of the method toward success is automatically deciding on the corners to classify them into two classes, good and malicious corners. Good corners are used to update the model in a conservative way removing the corners that are voting to the highly voted wrong positions due to the occluder. This leads to a continuous model learning during occlusion. Experimental results show a successful tracking along with a more precise estimation of shape and motion during occlusion