Human Activity Recognition Based-on Conditional Random Fields with Human Body Parts

摘要

The RGBD-based human activity recognition has captured extensive concerns of researchers from the domains of entertainment, surveillance, robotics and a variety of systems that involve interactions between persons and electronic devices. However, it is a non-trivial task due to the spatial and temporal variations in the activity data. Materials and Methods: This study propose a Conditional Random Fields (CRF) with star structure to model the variations and accurately recognize activity patterns. The human body is partitioned into five parts, the torso, the left arm, the right arm, the left leg and the right leg. Each vertex in this CRF model corresponds to one part of the human body in an activity sequence. Joint angle features are extracted to support this model. Results: This method not only takes advantage of multiple features and temporal context but also captures the spatial context among the human body parts. Conclusion: Experimental results show that this method achieved a higher recognition rate and it is still effective when self-occlusion happened.