  Human activity recognition in videos has been widely studied and has recently
gained significant advances 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
interactions 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 individual 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. We validate the
effectiveness of the proposed method using four widely used public datasets by
outperforming the competing state-of-the-art method.
