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Multiple objects tracking in unknown background using Bayesian estimation in 3D space

In this paper, we overview tracking methods of 3D occluded objects in 3D integral imaging. Two methods based on Summation of Absolute Difference (SAD) algorithm and Bayesian framework, respectively, are presented. For the tracking method based on SAD, we calculate SAD between pixels of consecutive frames of a moving object for 3D tracking. For the tracking method based on Bayesian framework, posterior probabilities of the reconstructed scene background and the 3D objects are calculated by defining their pixel intensities as Gaussian and Gamma distributions, respectively, and by assuming appropriate prior distributions for estimated parameters. Multi-objects tracking is achieved by maximizing the geodesic distance between the log-likelihood of the background and the objects. Experimental results demonstrate 3D tracking of occluded objects.