Depth map calculation for a variable number of moving objects using markov sequential object processes

M N M van Lieshout
IEEE Transactions on Pattern Analysis and Machine Intelligence 2008, 30 (7): 1308-12
We advocate the use of Markov sequential object processes for tracking a variable number of moving objects through video frames with a view towards depth calculation. A regression model based on a sequential object process quantifies goodness of fit; regularization terms are incorporated to control within and between frame object interactions. We construct a Markov chain Monte Carlo method for finding the optimal tracks and associated depths and illustrate the approach on a synthetic data set as well as a sport sequence.

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