Next Article in Journal
Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
Next Article in Special Issue
Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking
Previous Article in Journal
Activity Learning as a Foundation for Security Monitoring in Smart Homes
Previous Article in Special Issue
Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(4), 739;

Object Tracking Using Local Multiple Features and a Posterior Probability Measure

Systems Engineering Institute, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Author to whom correspondence should be addressed.
Received: 20 February 2017 / Revised: 17 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
Full-Text   |   PDF [3130 KB, uploaded 5 April 2017]   |  


Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods. View Full-Text
Keywords: object tracking; multiple features; posterior probability measure; centroid iteration object tracking; multiple features; posterior probability measure; centroid iteration

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).

Share & Cite This Article

MDPI and ACS Style

Guo, W.; Feng, Z.; Ren, X. Object Tracking Using Local Multiple Features and a Posterior Probability Measure. Sensors 2017, 17, 739.

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



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top