  Human action recognition is a well-known computer vision and pattern
recognition task of identifying which action a man is actually doing.
Extracting the keypoint information of a single human with both spatial and
temporal features of action sequences plays an essential role to accomplish the
task.In this paper, we propose a human action system for Red-Green-Blue(RGB)
input video with our own designed module. Based on the efficient Gated
Recurrent Unit(GRU) for spatio-temporal feature extraction, we add another
sampling module and normalization module to improve the performance of the
model in order to recognize the human actions. Furthermore, we build a novel
dataset with a similar background and discriminative actions for both human
keypoint prediction and behavior recognition. To get a better result, we
retrain the pose model with our new dataset to get better performance.
Experimental results demonstrate the effectiveness of the proposed model on our
own human behavior recognition dataset and some public datasets.
