For several years, keyboard and mouse have been used as the main interacting devices between users and computer games, but they are becoming outdated. Gesture-based human-computer interaction systems are becoming more popular owing to the emergence of virtual reality and augmented reality technologies. Therefore research on these systems has attracted a significant attention. The researches focus on designing the interactive interfaces between users and computers. Human-computer interaction is an important factor in computer games because it affects not only the experience of the users, but also the design of the entire game. In this research, we develop an particle filter-based face tracking method using color distributions as features, for the purpose of applying to gesture-based human-computer interaction systems for computer games. The experimental results proved the efficiency of particle filter and color features in face tracking, showing its potential in designing human-computer interactive games.

In this paper, I present my face detection and tracking method. First, image enhancement is carried out in HSV space especially if the input image is acquired from unconstrained illumination condition. I used a method for image enhancement in HSV space based on the local processing of image. I propose a lighting invariant face detection system based upon the edge and skin tone information of the input color image. The advantage of the proposed face detection is that, it can detect faces with different size, pose, and expression under unconstrained illumination conditions. I combined the Kalman filter with Camshift to enable track recovery after occlusions and to avoid the tracking failures caused by objects and background with similar colors to face. In my tracking method, I particularly focus on face tracking. The size and position of window are obtained after Camshift iteration. Kalman filtering is used to predict the next starting iterative point of Camshift. The experimental results show that my tracking method get the better results than Camshift in occlusion sequences and dynamic backgrounds.

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel’s position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The α- β - γ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity α - β filter, the α - β - γ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The α - β - γ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter’s performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, α - β - γ - η algorithm as compared to the constant acceleration model, α - β - γ in terms of error reduction and stability of the filter during target maneuver.

“Tracking” here refers to the estimation of a moving object with some degree of accuracy where at least one measurement is given. The measurement, which is the sensor-obtained output, contains systemic errors and errors that are due to the surrounding environment. Tracking filters play the key role of the target-state estimation after the updating of the tracking system; therefore, the type of filter that is used for the conduction of the estimations is crucial in the determining of the reliability of the updated value, and this is especially true since the performances of different filters vary when they are subjected to different environmental and initial conditions. The purpose of this paper is the conduction of a comparison between the performances of the α-β-γ filter and the Kalman filter regarding an ARPA-system tracking module that is used on board high-dynamic warships. The comparison is based on the capability of each filter to reduce noise and maintain a stable response. The residual error is computed from the difference between the true and predicted positions and the true and estimated positions for the given sample. The results indicate that the tracking accuracy of the Kalman filter is higher compared with that of the optimal α-β-γ filter; however, the response of the optimal α-β-γ filter is more stable.

방사선치료는 의료용 선형가속기를 이용하는 방식이 가장 많이 적용되고 있으며 사용되는 광자선도 고 에너지화 되고 있다. 최근에 도입되는 선형가속기에는 선속평탄 여과판을 사용하지 않는 3F 방식과 조사중 에 조사야에 맞춰 jaw가 움직이는 tracking jaw 기술이 적용되어 임상에 보급되고 있다. 이에 본 연구는 선 형가속기를 이용한 고에너지 X선 조사 시 선속평탄 여과판 사용 유무에 따른 광중성자 발생과, tracking ja w 사용에 따른 광중성자 발생을 측정하여 비교분석하였다. 그 결과 3F 방식이 선속평탄 여과판의 적용에 비하여 광중성자의 검출이 약 70% 낮게 나타났으며, tracking jaw 방식은 static jaw에 비하여 약 83% 높게 광중성자가 발생하는 것으로 나타났다.

For natural human-robot interaction, we need to know location and shape of facial feature in real environment. In order to track facial feature robustly, we can use the method combining particle filter and active appearance model. However, processing speed of this method is too slow. In this paper, we propose two ideas to improve efficiency of this method. The first idea is changing the number of particles situationally. And the second idea is switching the prediction model situationally. Experimental results is presented to show that the proposed method is about three times faster than the method combining particle filter and active appearance model, whereas the performance of the proposed method is maintained.

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.

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.

A purpose of developing a sound source tracking system in this paper is to reduce the noise efficiently from the received signal by microphone array and measure the signal's time delay between the microphones. I have applied the wavelet analysis algorithm to the system and calculated the sound source's relative position For the performance evaluation, I have compared with the results of utilizing the digital filtering methods based on the FIR LPF using Kaiser window function and the inverse Chebyshev IIR LPF. As a result, I have confirmed the fact that 'time-scale' filter using inverse discrete wavelet transform was suitable for this system.