Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects. Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process. Since that human actions and interacted objects provide strong context information, i.e. some actions are usually related to some specific objects, the accuracy of recognition is significantly improved for both of them. Through the proposed method, both global and local temporal features from skeleton sequences are extracted to model human actions. In the meantime, kernel features are utilized to describe interacted objects. Finally, all possible solutions from actions and objects are optimized by modeling the context between them. The results of experiments demonstrate the effectiveness of our method.