Video-based Human Action Recognition using Deep Learning: A Review

TL;DR

This paper reviews recent advances in video-based human action recognition using deep learning, analyzing models, results, and future challenges in the field.

Contribution

It provides a comprehensive overview of deep learning models for action recognition, highlighting current progress, advantages, disadvantages, and open research problems.

Findings

Identifies top-performing deep architectures for action recognition.

Analyzes recognition accuracies reported in recent literature.

Highlights open problems and future research directions.

Abstract

Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand, and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security, and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, and analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identifies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this field.