• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.639-642
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• 2005

융합과 필터링(Fusion and Filtering: F/F) 기법은 신호처리, 제어 등 많은 공학분야에서 사용되며 현재 파티클 필터(Particle Filter: PF)가 최근 가장 각광받고 있다. 그러나 비선형 시스템과 모델링 하기 어려운 에러조건 때문에 기존의 파티클 필터조차 제대로 다루지 못하는 공학환경이 존재한다. 이에 파티클 필터뿐만 아니라 칼만 계열(Kalman varieties)의 필터 방법들을 통합할 수 있는 Constraint Manifold(CM) 기반 융합과 필터링 방법이 제안되었다. 본 논문에서는 CM 기반 필터링을 효과적으로 수행할 수 있도록 제한 조건 표현에 대한 방법론을 제시하면 시뮬레이션을 통해 기존 파티클 필터와의 성능 비교를 수행하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.235-236
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• 2016

본 논문에서는 파티클 효과 응용에 대해서 나타낸다. 파티클 효과는 연기, 화염, 불꽃, 특수 효과를 이용하여 이러한 입자들을 표현하여 현실 세계에서 보다 더 현실감을 나타낼 수 있도록 한다. 본 연구에서는 이러한 파티클 효과를 적용함으로써 계절에 관계없이, 장소에 관계없이 콘텐츠를 제작하여 필요한 영상 분야나, 비디오 시각화 등 다양한 분야에서 사용될 수 있음을 나타내었고, 보다 많은 영역에서 특수 효과를 쉽게 연출 할 수 있음을 나타내었다. 따라서 실생활에서 다양한 파티클 효과를 재현함으로써 현실세계에서도 이러한 시뮬레이션을 구현 할 수 있음을 나타낸다. 또한 다양한 영역에서 콘텐츠로서 효과적으로 사용될 수 있다고 본다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.139-147
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• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.76-86
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• 2013

As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1273-1280
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• 2013

The UKF algorithm for tracking moving objects has fast convergence speed and good tracking performance without the derivative computation. However, this algorithm has serious drawbacks which limit its use in conditions such as Gaussian noise distribution. Meanwhile, the particle filter(PF) is a state estimation method applied to nonlinear and non-Gaussian systems without these limitations. But this method also has some disadvantages such as computation increase as the number of particles rises. In this paper, we propose the Unscented Particle Filter (UPF) algorithm which combines Unscented Kalman Filter (UKF) and Particle Filter (PF) in order to overcome these drawbacks.The performance of the UPF algorithm was tested to compare with Particle Filter using a 2-DOF (Degree of Freedom) Pendulum System. The results show that the proposed algorithm is more suitable to the nonlinear and non-Gaussian state estimation compared with PF.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.62-69
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• 2011

Development of sensing technology and progress of digital media have been creating new art genre named interactive media art. digital mirror working based on convergence between computer vision technology and video art, is expressing reconstituted spectator's visual image through various mediums. From this aesthetical point and high accessibility towards spectators, many types of digital mirrors have been introducing. However, the majority of digital mirrors express visual images unrelated to degree of spectator's participation and this caused obstruction to spectator's continuous participation and interaction. This paper proposes digital mirror operated by spectator's movements read through particle system synchronized with motion detection algorithm based on analyzing image difference. This work extracted the data of spectator's movement by image processing and designed particle system changed by this data. And it expressed reconstructed spectator's image.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.377-385
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• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.712-714
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• 2002

애니메이션에서 가장 중요하게 고려해야 할 문제는 프레임간 유사성을 어떻게 유지하느냐 이다. 각 프레임간 영상의 유사성이 없으면 프레임이 바뀔 때 좋지 못한 영상을 보여주기 때문이다. 또한 3차원 애니메이션을 위한 비사실적 렌더링에서는 프레임간 유사성뿐 아니라 렌더링을 수행하는 방법도 중요하다. 본 논문에서는 프레임간 유사성을 유지하기 위하여 파티클 시스템을 사용한다. 파티클을 물체의 실제 크기에 따라 분포시킴으로써 적절한 파티클의 수를 유지한다. 이때, 물체가 확대, 축소될 경우에는 화면상에서의 물체의 크기에 따라 동적으로 파티클의 수를 조정하게된다. 그리고 비사실적 렌더링을 위하여 붓의 터치를 표현할 스트로크를 사용하는데 스트로크의 방향, 색, 크기 등을 결정하기 위하여 참조 영상을 사용하는 렌더링 방법을 소개한다. 이렇게 결정된 스트로크들의 속성들은 붓 모양의 텍스쳐를 이용하여 렌더링 된다.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1022-1030
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• 2006

The location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet in spite of many researches. Among various location tracking systems, we choose the RFID system due to its wide applications. However, the sensed RSSI signal is too sensitive to the direction of a RFID reader antenna, the orientation of a RFID tag, the human interference, and the propagation media situation. So, the existing location tracking method in spite of using the particle filter is not working well. To overcome this shortcoming, we suggest a robust location tracking method with a double layered structure, where the first layer coarsely estimates a tag's location in the block level using a regression technique or the SVM classifier and the second layer precisely computes the tag's location, velocity and direction using the particle filter technique. Its layered structure improves the location tracking performance by restricting the moving degree of hidden variables. Many extensive experiments show that the proposed location tracking method is so precise and robust to be a good choice for implementing the location estimation of a person or an object in the ubiquitous computing. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complicate workplace.