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논문 기본 정보

자료유형
학술저널
저자정보
Seung-Min Park (중앙대학교) Junheong Park (중앙대학교) Hyung-Bok Kim (중앙대학교) Kwee-Bo Sim (중앙대학교)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.11 No.2
발행연도
2011.6
수록면
118 - 123 (6page)

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초록· 키워드

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Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

목차

Abstract
1. Introduction
2. System Overview
3. Background Image Update
4. Particle Filter
5. Experiment Results
6. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2013-028-000566980