• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.267-275
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• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• 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.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.80-84
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• 2010

Tracking hands is an essential technique for hand gesture recognition which is an efficient way in Human Computer Interaction (HCI). Recently, many researchers have focused on hands tracking using a 3D hand model and showed robust tracking results compared to using 2D hand models. In this paper, we propose a novel 3D hand tracking method based on a coupled particle filter. This provides robust and fast tracking results by estimating each part of global hand poses and local finger motions separately and then utilizing the estimated results as a prior for each other. Furthermore, in order to improve the robustness, we apply a multi-cue based method by integrating a color-based area matching method and an edge-based distance matching method. In our experiments, the proposed method showed robust tracking results for complex hand motions in a cluttered background.

• 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 Advanced Navigation Technology
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• v.16 no.5
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• pp.738-743
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• 2012

Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.363-365
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• 2012

데이터의 규모 및 활용도, 그리고 사용자 접근성 측면에서 실세계 데이터에서 가장 중요한 이슈가 되는 것은 비디오 데이터이다. 장르나 등장인물, 배경 등이 매우 상이한 대량의 비디오 데이터들이 등장하고 있기 때문에, 통일된 사전지식을 이용한 비디오 데이터 분석이 매우 비현실적이 되어가고 있으며 사전지식을 활용하지 않는 비디오 분석기법의 중요성이 커지고 있다. 본 논문에서는 진화 파티를 필터링과 우점 이미지를 이용하여 비디오 데이터를 분절(Segmentation)하는 기법을 소개한다. 이미지 분절화 과정에서 해결해야 할 난점은 시점 변화 및 움직임 등에 의해 발생하는 사소한 변화가 컴퓨터 관점에서는 무시하기 어려운 큰 변화로 해석될 수 있다는 점이다. 동일장면에서의 시점 변화와 같은 사소한 변화로 인하여 동일 세그먼트를 추정하지 못하는 어려움을 해결하기 위하여 우리는 이미지 일부를 표현하는 파티클의 개체군을 생성하여 협력적인 방식으로 개별 이미지 세그먼트를 표현하는 방법을 개발하였다. 또한 동일 인물의 움직임과 같은 변화에 대응할 수 있도록 진화 파티를 필터링 방법을 컬러 히스토그램 방법과 결합하여 추론 성능을 한층 개선하였다. 실제 TV 드라마에 대하여 수행된 인간 평가자의 분절 평가 결과와 비교하여 제안 방법의 성능을 확인하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.883-887
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• 2010

3D face tracking is one of essential techniques in computer vision such as surveillance, HCI (Human-Computer Interface), Entertainment and etc. However, 3D face tracking demands high computational cost. It is a serious obstacle to applying 3D face tracking to mobile devices which usually have low computing capacity. In this paper, to reduce computational cost of 3D tracking and extend 3D face tracking to mobile devices, an efficient particle filtering method using MLESAC(Maximum Likelihood Estimation SAmple Consensus) motion estimation is proposed. Finally, its speed and performance are evaluated experimentally.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.977-985
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• 2009

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.82-90
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• 2011

This paper proposes an advanced outdoor localization algorithm of a GPS(global positioning system)-INS(inertial navigation system) integration system. In order to reduce noise from the internal INS sensors, discrete wavelet transform and variable threshold method are utilized. The UPF (unscented particle filter) combines GPS information and INS signals to implement precise outdoor localization algorithm and to reduce noise caused by the acceleration, deceleration, and unexpected slips. The conventional de-noising method is mainly carried out using a low pass filter and a high pass filter which essentially result in signal distortions. This newly proposed system utilizes the vibration information of actuator according to fluctuations of the velocity to minimize signal distortions. The UPF also resolves non-linearities of the actuator and non-normal distributions of noises. Effectiveness of the proposed algorithm has been verified through the real experiments and the results are demonstrated.