Followed by the popularization of 3D TV, technical efforts to develop hologram TVs, known as real 3D displays, are being continuously made. However, the commercialization of hologram TV is taking a very long time due to the enormous difficulties in its appropriate technical realization. Nevertheless, several new services deploying the technology to produce hologram-like effect, instead of real hologram, have emerged. In this paper, as one such service, a game service with realistic holographic-like effect is proposed. This service utilizes head-tracked autostereoscopic technology with 360 degree panoramic video and can be delivered on IPTV. It will provide the possibility of employing the pseudo holographic technology as an immersive realistic game service in living room TV environments.

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

Finding a head of a person in a scene is very important for taking a well composed picture by a robot photographer because it depends on the position of the head. So in this paper, we propose a robust head tracking algorithm using a hybrid of an omega shape tracker and local binary pattern (LBP) AdaBoost face detector for the robot photographer to take a fine picture automatically. Face detection algorithms have good performance in terms of finding frontal faces, but it is not the same for rotated faces. In addition, when the face is occluded by a hat or hands, it has a hard time finding the face. In order to solve this problem, the omega shape tracker based on active shape model (ASM) is presented. The omega shape tracker is robust to occlusion and illumination change. However, when the environment is dynamic, such as when people move fast and when there is a complex background, its performance is unsatisfactory. Therefore, a method combining the face detection algorithm and the omega shape tracker by probabilistic method using histograms of oriented gradient (HOG) descriptor is proposed in this paper, in order to robustly find human head. A robot photographer was also implemented to abide by the 'rule of thirds' and to take photos when people smile.