Human Body Pose Recognition from a Single-View Depth Camera
Date Issued
2012
Date
2012
Author(s)
Huang, Po-Chi
Abstract
Recognizing human body poses is a challenging task because of varied human poses and unpredictable human movement. To address these problems, we propose a model-based approach for human body pose recognition from a single-view depth camera. The proposed algorithm applies an articulated cylinder model to detect human pose and track them based on a particle filter without numerous training data or heuristic detectors. To reduce high degrees of freedom, we adopt a hierarchical method that detects torso and limbs successively. Moreover, we take the advantage of a particle filter to track complex human motion and the results show that the proposed system is robust in human motion tracking. The qualitative evaluation shows that our method can deal with self-occlusion problem and ambiguous human motion effectively, and the quantitative evaluation shows that the average tracking error is 0.06 meters with a standard deviation of 0.04 meters. The proposed method tracks human poses successfully at the speed of 20 frames per second on a laptop with Intel Core i3-2100 CPU and without graphic processing unit.
Subjects
human body pose recognition
human motion tracking
particle filter
human-computer interaction
depth image analysis
Type
thesis
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