Bayesian tracking fusion framework with online classifier ensemble for immersive visual applications

During the last decade, the development of the immersive virtual reality (VR) has achieved a great progress in different application areas. For more advanced large-scale immersive VR environments or systems, one of the most challenge is to accurately track the position of the user’s body part such as head when he/she is immersived in the environment to feel the changes among the synthetic stereoscopic image sequences. Unfortunately, accurate tracking is not easy in the virtual reality scenarios due to the variety types of existing intrinsic and extrinsic changes when tracking is on-the-fly. Especially for the single tracker, a long time accurate tracking is usually not possible because of the model adaption problem in different environments. Recent trend of research in tracking is to incorporate multiple trackers into a compositive learning framework and utilize the advantages of different trackers for more effective tracking. Therefore, in this paper, we propose a novel Bayesian tracking fusion framework with online classifier ensemble strategy. The proposed tracking formulates a fusion framework for online learning of multiple trackers by modeling a cumulative loss minimization process. With an optimal pair-wise sampling scheme for the SVM classifier, the proposed fusion framework can achieve more accurate tracking performance when compared with the other state-of-art trackers. In addition, the experiments on the standard benchmark database also verify that the proposed tracking is able to handle the challenges in many immersive VR applications and environments.