Predicting multiple target tracking performance for applications on video sequences

Predicting multiple target tracking performance for applications on video sequences This paper presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker and Markov Chain Monte Carlo data association methods are used as test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

Predicting multiple target tracking performance for applications on video sequences

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0840-8
Publisher site
See Article on Publisher Site

Abstract

This paper presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker and Markov Chain Monte Carlo data association methods are used as test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Apr 29, 2017

References

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