A sifting mechanism based object localization algorithm for fast probabilistic occupancy map (sifted probabilistic occupancy map, SPOM) is proposed to calculate the positions of moving objects fast and accurately in typical multi-view surveillance scenarios. Specifically, an efficient sifting mechanism is designed firstly to roughly estimate the 3D positions of the moving objects according to the output of motion detection. Secondly, a proper likelihood model is set up by Bayesian method to calculate the occupancy probability of the objects for each position within the sifted region. Finally, object positions are obtained according to a pre-set threshold of probabilistic occupancy map, and particle filter is utilized to adjust the results to improve the localization accuracy. Compared with the conventional probabilistic occupancy map (POM), the proposed method can decrease the computational overload dramatically by discarding the impossible object positions through the sifting process, therefore the running speed is improved, and it simultaneously provides more accurate estimations for object positions. Based on the self-built experimental platform, comparative experiments of SPOM and POM are conducted, and the obtained results demonstrate that the proposed method can locate the moving objects more quickly and accurately.
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