Distributed Detection Methods for Displacement Estimation

In this paper, a distributed detection approach for displacement estimation in image sequences is presented. This method is derived from a Bayesian framework and reduces to a M-ary Hypothesis test among a representative set of possible displacement vectors. It is shown that the mean-squared error based block-matching (BM) algorithm is a special case of this general approach. In our approach, at each point of the current frame a set of overlapping localized detectors outputs a number of estimates for the displacement vector. Then, a distributed detection network is adopted for the fusion of the these estimates. Since the computational load is high, suboptimal but computationally efficient solutions are proposed. The above method gives a more accurate estimation of the displacement field and it is shown to be more robust in the presence of occlusion and noise, compared to the BM algorithm. Experimental results on video-conference image sequences are presented.