  Human action recognition (HAR) in videos is a fundamental research topic in
computer vision. It consists mainly in understanding actions performed by
humans based on a sequence of visual observations. In recent years, HAR have
witnessed significant progress, especially with the emergence of deep learning
models. However, most of existing approaches for action recognition rely on
information that is not always relevant for this task, and are limited in the
way they fuse the temporal information. In this paper, we propose a novel
method for human action recognition that encodes efficiently the most
discriminative appearance information of an action with explicit attention on
representative pose features, into a new compact grid representation. Our GRAR
(Grid-based Representation for Action Recognition) method is tested on several
benchmark datasets demonstrating that our model can accurately recognize human
actions, despite intra-class appearance variations and occlusion challenges.
