Next Article in Journal
Best Practices Kits for the ICT Governance Process within the Secretariat of State-Owned Companies of Brazil and Regarding these Public Companies
Previous Article in Journal
Multiple Congestion Points and Congestion Reaction Mechanisms for Improving DCTCP Performance in Data Center Networks
Article Menu

Export Article

Open AccessArticle
Information 2018, 9(6), 140; https://doi.org/10.3390/info9060140

Target Tracking Algorithm Based on an Adaptive Feature and Particle Filter

1
College of Engineering, Huaqiao University, Quanzhou 362021, China
2
University Engineering Research Center of Fujian Province Industrial Intelligent Technology and Systems, Huaqiao University, Quanzhou 362021, China
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 6 June 2018 / Accepted: 7 June 2018 / Published: 8 June 2018
(This article belongs to the Section Information Processes)
Full-Text   |   PDF [3132 KB, uploaded 14 June 2018]   |  

Abstract

To boost the robustness of the traditional particle-filter-based tracking algorithm under complex scenes and to tackle the drift problem that is caused by the fast moving target, an improved particle-filter-based tracking algorithm is proposed. Firstly, all of the particles are divided into two parts and put separately. The number of particles that are put for the first time is large enough to ensure that the number of the particles that can cover the target is as many as possible, and then the second part of the particles are put at the location of the particle with the highest similarity to the template in the particles that are first put, to improve the tracking accuracy. Secondly, in order to obtain a sparser solution, a novel minimization model for an Lp tracker is proposed. Finally, an adaptive multi-feature fusion strategy is proposed, to deal with more complex scenes. The experimental results demonstrate that the proposed algorithm can not only improve the tracking robustness, but can also enhance the tracking accuracy in the case of complex scenes. In addition, our tracker can get better accuracy and robustness than several state-of-the-art trackers. View Full-Text
Keywords: target tracking; particle filtering; intelligent particle; Lp norm; multi-feature fusion target tracking; particle filtering; intelligent particle; Lp norm; multi-feature fusion
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lin, Y.; Huang, D.; Huang, W. Target Tracking Algorithm Based on an Adaptive Feature and Particle Filter. Information 2018, 9, 140.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top