Adaptive Non-rigid Object Tracking by Fusing Visual and Motional Descriptors

This paper presents a framework to track non-rigid objects adaptively by fusion of visual and motional feature descriptors. The proposed technique can automatically detect an object from different points of view as soon as the object starts moving. Moreover an object model is created and gradually updated using both new and previous features. As a result, the proposed technique is able to track a non-rigid object even if the object is rotating or distorting. The efficacy of the proposed method is verified using the experimental results obtained from a grayscale camera.