Three-dimensional Human Arms Online Tracking with a Single Camera
Date Issued
2011
Date
2011
Author(s)
Tu, Ming-Han
Abstract
This research presents a 3-D human arm tracking method with a monocular camera. In order to integrate the tracking system with the robot interface, the ability to achieve a real time performance is desired. Multiple clues are integrated by the multiple importance sampling (MIS) particle filter to track the arms with arbitrary motion. Due to the lack of depth information on a monocular camera, a sequential pose estimation based on structure-from-motion (SFM) is proposed to online estimate 3D posture of the arms. The estimation result is then designed as one of the proposal functions of MIS particle filter to generate the 3D posture hypotheses. In order to obtain a more reliable recovery of the motion from online resolution of the SFM, the structure of each body parts is priory assumed to be volumetric model. The feature points, which are spread according to the volumetric model of each body parts, are tracked to measure the image displacement for online solving the 3D motion. Additionally, while the objects in view appear larger when they are closer to the camera, the size effect from perspective projection is considered to disambiguate the arm posture. Finally, the hypotheses from the multiple importance sampling are verified by 2D visual features and the 3D posture information augmented with the size effect. Experiments show the robustness and efficiency of the proposed algorithm.
Subjects
Particle Filter
Visual Tracking
Structure-from-motion
Arms Tracking
Type
thesis
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