Extracting moving shapes by evidence gathering

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Description/Abstract

Many approaches can track objects moving in sequences of images but can suffer in occlusion and noise, and often require initialisation. These factors can be handled by techniques that extract objects from image sequences, especially when phrased in terms of evidence gathering. Since the template approach is proven for arbitrary shapes, we re-deploy it for moving arbitrary shapes, but in a way aimed to avoid discretisation problems. In this way, the discrete mapping operation is deferred as far as possible, by using continuous shape descriptions. A further advantage is reduction in computational demand, as seen in use of templates for shape extraction. This prior specification of motion avoids the need to use an expensive parametric model to capture data that is already known. Furthermore, the complexity of the motion template model remains unchanged with increase in the complexity of motion, whereas a parametric model would require increasingly more parameters leading to an enormous increase in computational requirements. The new approach combining moving arbitrary shape description with motion templates permits us to achieve the objective of low dimensionality extraction of arbitrarily moving arbitrary shapes with performance advantage as reflected by the results this new technique can achieve.