Multimedia Tools and Applications
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segmentation of moving regions in outdoor environment under a moving camera is a fundamental step in many vision systems including automated visual surveillance, human-machine interface, tracking etc. It is also a chal- lenging task due to camera motion, object motion, and out- door scene challenges i.e. periodic motions of swaying of trees, gradual illumination changes, etc. In this paper, a wide area scene modeling approach for object segmenta- tion under a moving camera is proposed. This approach suffers due to parallax effect, misallignment errors etc and needs their concurrent removal for its success. we explic- itly model the dense correspondence between input image and panoramic background model. Foreground segmenta- tion and correspondence estimation are expressed as a uni- fied labeling problem, and are solved efficiently via tree dy- namic programming (TDP). Lucas-Kanade method is used to find sparse correspondence between image and model, and robust M-estimator is then applied to find the projective transformation for initialization of TDP's window search. Optimal dense correspondences are achieved and are used to update panoramic background model and as a byprod- uct, online refined panoramic image is generated which is empty in the beginning and is filled step by step. We test our algorithm with hand-held camera and also with a camera mounted on a moving platform. Experiments proved our algorithm to be robust in performance