Adaptive Hybrid Mean Shift and Particle Filter

Phong Le, Duong Anh Duc, Vu Hai Quan, Nam Trung Pham

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

Abstract

The changing of dynamic models in object tracking can cause high errors in state estimation algorithms. In this paper, we propose a method, Adaptive Hybrid Mean Shift and Particle Filter (AHMSPF), to solve this problem. AHMSPF consists of three stages. First, the mean shift algorithm is employed to search an object candidate near the target state. Then, if this candidate is good enough, it will be used to adapt the particle filter parameters. Finally, the particle filter will estimate the target state based on these new parameters. Experimental results shown that our method has a better performance than the traditional particle filter.
Original languageEnglish
Title of host publication2009 IEEE-RIVF International Conference on Computing and Communication Technologies
Place of PublicationDa Nang, Vietnam
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-4
Number of pages4
ISBN (Electronic)978-1-4244-4568-4
ISBN (Print)978-1-4244-4566-0
DOIs
Publication statusPublished - 28 Jul 2009
Event2009 IEEE-RIVF International Conference on Computing and Communication Technologies - Danang, Viet Nam
Duration: 13 Jul 200917 Jul 2009

Conference

Conference2009 IEEE-RIVF International Conference on Computing and Communication Technologies
Country/TerritoryViet Nam
CityDanang
Period13/07/0917/07/09

Fingerprint

Dive into the research topics of 'Adaptive Hybrid Mean Shift and Particle Filter'. Together they form a unique fingerprint.

Cite this