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Probabilistic Background Model by Density Forests for Tracking

Probabilistic Background Model by Density Forests for Tracking
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Author(s): Daimu Oiwa (Department of Computer Science, Chubu University, Kasugai, Japan), Shinji Fukui (Department of Computer Science, Aichi University of Education, Kariya, Japan), Yuji Iwahori (Department of Computer Science, Chubu University, Kasugai, Japan), Tsuyoshi Nakamura (Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Japan), Boonserm Kijsirikul (Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand)and M. K. Bhuyan (Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, India)
Copyright: 2017
Volume: 5
Issue: 2
Pages: 16
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2017040101

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Abstract

This paper proposes an approach for a robust tracking method to the objects intersection with appearances similar to a target object. The target is image sequences taken by a moving camera in this paper. Tracking methods using color information tend to track mistakenly a background region or an object with color similar to the target object since the proposed method is based on the particle filter. The method constructs the probabilistic background model by the histogram of the optical flow and defines the likelihood function so that the likelihood in the region of the target object may become large. This leads to increasing the accuracy of tracking. The probabilistic background model is made by the density forests. It can infer a probabilistic density fast. The proposed method can process faster than the authors' previous approach by introducing the density forests. Results are demonstrated by experiments using the real videos of outdoor scenes.

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