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Human activity recognition in videos has been widely studied and has recentlygained significant advances with deep learning approaches; however, it remainsa challenging task. In this paper, we propose a novel framework thatsimultaneously considers both implicit and explicit representations of humaninteractions by fusing information of local image where the interactionactively occurred, primitive motion with the posture of individual subject'sbody parts, and the co-occurrence of overall appearance change. Humaninteractions change, depending on how the body parts of each human interactwith the other. The proposed method captures the subtle difference betweendifferent interactions using interacting body part attention. Semanticallyimportant body parts that interact with other objects are given more weightduring feature representation. The combined feature of interacting body partattention-based individual representation and the co-occurrence descriptor ofthe full-body appearance change is fed into long short-term memory to model thetemporal dynamics over time in a single framework. We validate theeffectiveness of the proposed method using four widely used public datasets byoutperforming the competing state-of-the-art method.

Human Interaction Recognition Framework based on Interacting Body Part Attention