Human interaction recognition is an essential task in video surveillance.The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without ...Human interaction recognition is an essential task in video surveillance.The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people.In this paper,we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene.To address this problem,we propose an Interactive Relation Embedding Network(IRE-Net)to simultaneously identify the subjects involved in the interaction and recognize their interaction category.As a new problem,we also build a new dataset with annotations and metrics for performance evaluation.Experimental results on this datasesthow significant improvements of the proposed method when compared with current methodsdeveloped for human interaction recognition and group activity recognition.展开更多
基金This work was supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.62072334,U1803264).
文摘Human interaction recognition is an essential task in video surveillance.The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people.In this paper,we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene.To address this problem,we propose an Interactive Relation Embedding Network(IRE-Net)to simultaneously identify the subjects involved in the interaction and recognize their interaction category.As a new problem,we also build a new dataset with annotations and metrics for performance evaluation.Experimental results on this datasesthow significant improvements of the proposed method when compared with current methodsdeveloped for human interaction recognition and group activity recognition.
