Motion and Pose estimation are a widely studied research problem area in the field of computer vision. Despite major progress that has been made in pursuing this area, pose estimation is still a largely unsolved problem. Many challenging, practical, and real-world applications need to be taken into consideration when developing new estimation solutions for this research area. These challenges include low Signal to Noise Ratio (SNR), local optimal solutions, and other related practical issues. Therefore, accurate and robust pose estimation solutions are needed, especially for time-critical and sensitive applications such as medical surgeries and space robot applications.In this thesis, we focus on one important class of pose estimation solutions that are based on fusion techniques. We focus on solutions that exploit depth cues, color information, and wearable sensor, which can be fused to enhance accuracy and robustness of the pose estimation system. Furthermore, we explore graphical inference models, such as Loopy Belief Propagation methods, that can enhance pose and motion estimation accuracy. Consequently, in this thesis, we present our findings regarding various fusion techniques and graphical inference methods to solve the pose estimation problem. We further apply these techniques in the estimation of Parkinson's Disease tremors, rigid object pose estimation, and robot localization. Additionally, we have developed a prototype by using Kinect v2, which is capable of tracking motion of Parkinson's Disease patients. The proposed system can lead to cost-effective and efficient motion tracker for medical applications.
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