• 로봇학회논문지
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• 제2권1호
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• pp.21-28
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• 2007

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.46-51
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• 2005

Intelligent Space(ISpace) is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

• 한국산업융합학회 논문집
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• 제27권2_2호
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• pp.319-324
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• 2024

In this paper, the Artificial Intelligence Space(AI-Space) for human-robot interface is presented, which can enable human-computer interfacing, networked camera conferencing, industrial monitoring, service and training applications. We present a method for representing, tracking, and objects(human, robot, chair) following by fusing distributed multiple vision systems in AI-Space. The article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguous conditions. We propose to track the moving objects(human, robot, chair) by generating hypotheses not in the image plane but on the top-view reconstruction of the scene.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.444-450
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• 2024

Based on multiple measurements of ionization loss, the Time Projection Chamber (TPC) combines strong tracking ability with particle identification ability in a large momentum range, which is an important advantage of TPC detection technology over traditional ionization measurement technology. According to these two characteristics of TPC, applying it to the measurement of fission cross-section can greatly improve the measurement accuracy. During the measurement of the fission cross-section, the number of target nuclei is required to be accurately measured. So this paper introduces a method for measuring the number of ²³⁵U target nuclei using a fission TPC system. The measurement result agrees with the reference value, and relative error is around 1 %.

• 디지털콘텐츠학회 논문지
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• 제11권4호
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• pp.597-606
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• 2010

본 논문에서는 증강현실 환경에서 보다 강건한 카메라 정보 추정을 위한 입자필터 기반의 카메라 추적 기법에 대해서 설명한다. 실시간 카메라 추적을 위해서는 일반적으로 칼만 필터, 또는 확장 칼만 필터 등이 많이 이용되지만, 카메라의 급격한 흔들림 및 장면의 가려짐 등과 같은 불안정한 조건에서는 정상적인 카메라 추적이 매우 힘들다는 단점이 있다. 본 논문에서 제안하는 입자필터링 기법은 시스템 상태에 대한 측정 표본입자의 가중치를 별도의 가중치 계산과정을 이용하지 않고 가우스 분포를 기반으로 계산하였으며, 카메라 입자를 수렴시키기 위한 별도의 처리과정을 거치지 않고 시스템의 실제 불확실도에 근사화되도록 재표본화된 표본입자 집합을 이용하여 카메라 상태의 추정을 수행하였다. 또한 제안된 방법은 보다 많은 수의 표본 입자를 이용하는 환경에서도 실시간 처리가 가능한 장점이 있다. 실험을 통하여 다양한 환경 하에서 제안된 방법의 효율성과 정확성을 확인하였다.