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Sensors 2017, 17(4), 739; doi:10.3390/s17040739

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
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Received: 20 February 2017 / Revised: 17 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
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Abstract

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
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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).

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

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