Action Recognition Based on Machine Learning under Multiple Motion Sensing Cameras

Recognition and analysis of human action are one of the most popular issues in the computer vision and pattern recognition community.Human action consists of consecutive static poses and contains complicated highdimensional information in spatial-temporal domain. There still exist bottlenecks in precise analysis of human action by single view RGB-D camera due to technical problems, like the self-shadowing phenomenon of human poses.Intuitively, human skeleton is appropriate representation of human profile. So we propose the scenarios using multi RGB-D cameras to resolve the self-shadowing problem and adopting the human skeleton axis for object tracking and action recognition.In the proposed system, human skeleton detection of open source is adopted for the basis of action recognition and we can get a large amount of metadata. Machine learning technique is used to classify these metadata to build our own dataset.We expect that the effort will have positive impact on various applications like anti-theft security, home care, physical rehabilitation, and traffic accident prevention. The research results can not only facilitate the relevant studies but also motivate commercial opportunities.