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Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes
July 2008 (vol. 30 no. 7)
pp. 1308-1312
ASCII Text
x 
 
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 and Machine Intelligence, vol. 30, no. 7, pp. 1308-1312, July, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.45,
author = {M.N.M. van Lieshout},
title = {Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {7},
issn = {0162-8828},
year = {2008},
pages = {1308-1312},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.45},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes
IS - 7
SN - 0162-8828
SP1308
EP1312
EPD - 1308-1312
A1 - M.N.M. van Lieshout,
PY - 2008
KW - Vision and Scene Understanding
KW - Motion
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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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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 and Machine Intelligence, vol. 30, no. 7, pp. 1308-1312, July 2008, doi:10.1109/TPAMI.2008.45
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