An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker

Kusha Panta, [No Value] Ba-Ngu-Vo, Daniel E. Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a closed-form solution for the probability hypothesis density (PHD) filter, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter and miss-detections. Recently, a GM-PHD tracker based on the GM-PHD filter has been proposed to correctly maintain temporal association amongst target estimates by tagging individual Gaussian components, and to provide estimates of individual target trajectories and their identities. In this paper, we propose a tag and a track management scheme for the GM-PHD tracker, which is computationally efficient and provides a framework for parallel processing of data. Based on the proposed scheme, we also present a number of simpler and efficient pruning schemes for Gaussian components. ©2006 IEEE.

Original languageEnglish
Title of host publicationProceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006
Pages230-235
Number of pages6
DOIs
Publication statusPublished - 2006
Event4th International Conference on Intelligent Sensing and Information Processing - Bangalore, India
Duration: 15 Dec 200618 Dec 2006

Conference

Conference4th International Conference on Intelligent Sensing and Information Processing
Abbreviated titleICISIP 2006
CountryIndia
CityBangalore
Period15/12/0618/12/06

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Trajectories
Processing
Uncertainty

Cite this

Panta, K., Ba-Ngu-Vo, N. V., & Clark, D. E. (2006). An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. In Proceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006 (pp. 230-235) https://doi.org/10.1109/ICISIP.2006.4286102
Panta, Kusha ; Ba-Ngu-Vo, [No Value] ; Clark, Daniel E. / An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. Proceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006. 2006. pp. 230-235
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Panta, K, Ba-Ngu-Vo, NV & Clark, DE 2006, An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. in Proceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006. pp. 230-235, 4th International Conference on Intelligent Sensing and Information Processing, Bangalore, India, 15/12/06. https://doi.org/10.1109/ICISIP.2006.4286102

An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. / Panta, Kusha; Ba-Ngu-Vo, [No Value]; Clark, Daniel E.

Proceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006. 2006. p. 230-235.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Panta K, Ba-Ngu-Vo NV, Clark DE. An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. In Proceedings - 4th International Conference on Intelligent Sensing and Information Processing, ICISIP 2006. 2006. p. 230-235 https://doi.org/10.1109/ICISIP.2006.4286102