Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes

Author

van Lieshout, M.N.M.

Author_Institution

Centrum voor Wiskunde en Inf., Amsterdam

Volume

30

Issue

7

fYear

2008

fDate

7/1/2008 12:00:00 AM

Firstpage

1308

Lastpage

1312

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.

Keywords

Markov processes; Monte Carlo methods; image motion analysis; image sequences; object detection; video signal processing; Markov chain Monte Carlo method; Markov sequential object processes; regression model; sports sequence; video frames; video sequence; Biomedical measurements; Equations; Geometry; Layout; Monte Carlo methods; Noise robustness; Phase measurement; Stochastic processes; Tracking; Transforms; Motion; Vision and Scene Understanding; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Markov Chains; Motion; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Video Recording;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2008.45

Filename

4459333