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

M.N.M. Lieshout, van

doi:10.1109/TPAMI.2008.45

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

M.N.M. Lieshout, van

Research output: Contribution to journal › Article › Academic › peer-review

8 Citations (Scopus)

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 sports sequence.

Original language	English
Pages (from-to)	1308-1312
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	30
Issue number	7
DOIs	https://doi.org/10.1109/TPAMI.2008.45
Publication status	Published - 2008

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10.1109/TPAMI.2008.45

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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 sports sequence.",

author = "{Lieshout, van}, M.N.M.",

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AB - 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 sports sequence.

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

Abstract

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