The Community for Technology Leaders
Green Image
ABSTRACT
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.
INDEX TERMS
Vision and Scene Understanding, Motion
CITATION
M.N.M. van Lieshout, "Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1308-1312, July 2008, doi:10.1109/TPAMI.2008.45
88 ms
(Ver 3.1 (10032016))