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Moving object segmentation based on optical flow field

Traditional optical flow estimation methods have two drawbacks: firstly, flow estimation is not accurate enough on border of the target which result in the blurring there; secondly, with the increasing of the speed of the object motion, the estimation error of brightness constancy assumption will be also increased. Focusing on the above two points, an improved optical flow estimation method is presented in this paper. To alleviate flow constraint errors, we employed a re-weighted least-squares method to suppress unreliable flow constraints, thus leading to robust estimation of optical flow. In addition, a coarse-to-fine adjustment scheme is proposed to refine the optical flow estimation especially for large image motions. We also proposed an algorithm for target segmentation of image sequences based on clustering in the feature vector space. Experimental results on some synthetic and real image sequences showed that, the proposed algorithm has favorable performance comparing with the existed methods in terms of accuracy and computation cost. Furthermore, the segmentation results based on the proposed method can be obtained in the case of complicated background.