적응적 관측 모델과 파티클 필터를 이용한 객체 추적
A Method of Tracking Object using Particle Filter and Adaptive Observation
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초록
In this paper, we propose an efficient method that is tracking an object in real time using particle filter and adaptive observation model. When tracking object, it happens object shape variation by camera or object movement in variety environments. The traditional method has an error of tracking from these variation, because it has fixed observation model about the selected object by the user in the initial frame. In order to overcome these problems, we propose a method that updates the observation model by calculating the similarity between the used observation model and the eight-way of edge model from the current position. If the similarity is higher than the threshold value, tracking the object using updated observation model to reset observation model. On the contrary to this, the algorithm which consists of a process is to maintain the used observation model. Finally, this paper demonstrates the performance of the stable tracking through comparison with the traditional method by using a number of experimental data.
Abstract
In this paper, we propose an efficient method that is tracking an object in real time using particle filter and adaptive observation model. When tracking object, it happens object shape variation by camera or object movement in variety environments. The traditional method has an error of tracking from these variation, because it has fixed observation model about the selected object by the user in the initial frame. In order to overcome these problems, we propose a method that updates the observation model by calculating the similarity between the used observation model and the eight-way of edge model from the current position. If the similarity is higher than the threshold value, tracking the object using updated observation model to reset observation model. On the contrary to this, the algorithm which consists of a process is to maintain the used observation model. Finally, this paper demonstrates the performance of the stable tracking through comparison with the traditional method by using a number of experimental data.
참고문헌 (10)
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- E. Orhan, "Particle Filtering" (2012), Center for Neural Science, University of Rochester
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- Lowe, David G, "Object recognition from local scale-invariant features", Computer vision, 1999. The proceedings of the seventh IEEE international conference, 2 (1999)
- Tran, Antoine, "versatile object tracking algorithm combining Particle Filter and Generalised Hough Transform", Image Processing Theory, Tools and Applications (IPTA), November, 2015 International Conference, pp.105~110 (2015), IEEE