Human interaction recognition framework based on interacting body part attention

Abstract

Human activity recognition in videos has been widely studied and has recently gained significant ad -vances with deep learning approaches; however, it remains a challenging task. In this paper, we propose a novel framework that simultaneously considers both implicit and explicit representations of human in-teractions by fusing information of local image where the interaction actively occurred, primitive motion with the posture of individual subject's body parts, and the co-occurrence of overall appearance change. Human interactions change, depending on how the body parts of each human interact with the other. The proposed method captures the subtle difference between different interactions using interacting body part attention. Semantically important body parts that interact with other objects are given more weight during feature representation. The combined feature of interacting body part attention-based individ-ual representation and the co-occurrence descriptor of the full-body appearance change is fed into long short-term memory to model the temporal dynamics over time in a single framework. The experimen-tal results on five widely used public datasets demonstrate the effectiveness of the proposed method to recognize human interactions from videos. (c) 2022 Elsevier Ltd. All rights reserved.