Spatio-Temporal Human Action Recognition Modelwith Flexible-interval Sampling and Normalization

TL;DR

This paper introduces a novel spatio-temporal human action recognition model utilizing flexible-interval sampling and normalization, enhancing performance on custom and public datasets through a specialized RGB video system.

Contribution

The paper presents a new human action recognition system with a custom dataset, incorporating a sampling and normalization module to improve spatio-temporal feature extraction.

Findings

Effective action recognition on custom dataset

Improved performance with sampling and normalization modules

Successful application to public datasets

Abstract

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.