• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.552-555
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• 2012

The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

• 정보과학회 논문지
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• 제42권2호
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• pp.190-202
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• 2015

스마트폰내 GPS 및 다양한 센서 데이터를 이용하여 스마트폰 사용자의 이동 패턴을 학습하고, 이를 기반으로 사용자 목적지와 경로를 예측하여 사용자의 의도에 맞는 서비스를 제공하는 위치기반 지능형 개인화 서비스(Intelligent personal assistant) 연구가 활발히 진행 되고 있다. 위치기반 개인화 서비스의 지능성은 불완전한 센서 데이터로부터 사용자 이동 정보를 처리하여, 실시간으로 사용자의 경로를 예측하는 정확성과 효율성에 좌우된다. 본 논문은 불완전한 정보로부터 사용자의 경로와 목적지를 추론하는 동적 베이지안 네트워크 기반의 강인한 파티클 필터(Robust particle filter)를 제안한다. 제안한 강인한 파티클 필터 방법은 부정확하고, 불완전한 센서 정보를 보완할 수 있는 파티클 생성, 실시간에 계산 복잡도를 감소시키는 효율적인 스위칭 함수와 가중치 함수, 파티클의 정확도를 향상시키는 재표본화로 구성되며, 사용자의 목적지와 경로의 예측 정확성과 효율성의 성능을 향상시켰다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2088-2091
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• 2003

We presents a new algorithm for tracking person in video sequence that integrates the meanshift iteration procedure into the particle filtering. Utilizing the nice property of convergence to the modes in the meanshift iteration we show that only a few sample points are sufficient, while in general the particle filtering requires a large number of sample points. Multi-parts of a person is tracked independently of each other based on the color Then, the similarity against the reference model color and the geometric constraints between multi-parts are reflected as the sample weights. Also presented is the computer simulation results, which show successful tracking even for complex background clutter.

• 대한전자공학회논문지SP
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• 제48권5호
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• pp.16-22
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• 2011

본 논문에서는 배경 차분에 의해 객체를 검출하고 확률적으로 표본화된 입자 필터링(particle filtering)기법을 사용한 다중객체 추적 기법을 제안한다. 확률적으로 표본화된 입자들을 사용하여 다중 객체에 독립적으로 적용할 때 발생하는 계산 복잡도(computational complexity)를 감소시키는 동시에 안정적인 추적을 가능하게 하였다. 객체의 색상정보를 사용한 히스토그램 분포에 의한 관측 모델(observation model)을 구성하고 객체의 움직임 정보를 위해 동적 모델을 공식화하여 영상을 해석하였다. 전체적인 추적 시스템은 베이시언 최대 우도 기법(Bayesian maximum likelihood method)을 근간으로 하되, 입자 필터링을 객체 추적에 적용하여 실용적인 현실 객체 추적 상황에도 강건하게 대처할 수 있음을 실험을 통해서 증명하였다.

• 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.

• 전기학회논문지
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• 제56권10호
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• ETRI Journal
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• 제37권3호
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• pp.551-561
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• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• 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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• 제54권6호
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.