Study of Multi-target Tracking Algorithm Based on Mean-shift and Particle Filter

Lijing Huang (), Naiwen Yu (), Ming Han and Peng Liu
Additional contact information
Lijing Huang: Hebei University of Science and Technology
Naiwen Yu: Hebei University of Science and Technology
Ming Han: Yanshan University
Peng Liu: Hebei University of Science and Technology

A chapter in LISS 2014, 2015, pp 1717-1724 from Springer

Abstract: Abstract Target tracking in video sequences is an important part of information management. To combine the advantages of mean-shift tracking algorithm’s real-time and particle filter tracking algorithm’s robustness, this paper proposes a kind of particle filter multi-target tracking algorithm that based on weighted mean-shift. Firstly, we introduce non-parametric fast pattern matching algorithm of Kernel density estimation into particle filter, and iteratively calculate the probability density estimation. Then, the particle gradient direction is estimated by mean shift, and the mean for each particle moved to the sample is calculated. When the position of particle is changed, the weighted processing of the resampling particles will be done. Finally, sampling the new particles based on our algorithm. That can solve the particle degradation phenomena effectively and improve the status estimation accuracy. The algorithm that we proposed has been applied to multi-target tracking, and the experiments have shown the feasibility and effectiveness of the algorithm.

Keywords: Posterior distribution; Density estimation; Mean-shift; Weighted value; Particle filter (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-43871-8_247

Ordering information: This item can be ordered from
http://www.springer.com/9783662438718

DOI: 10.1007/978-3-662-43871-8_247

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().