Video-based Human Action Recognition using Deep Learning: A Review
- URL: http://arxiv.org/abs/2208.03775v1
- Date: Sun, 7 Aug 2022 17:12:12 GMT
- Title: Video-based Human Action Recognition using Deep Learning: A Review
- Authors: Hieu H. Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A.
Velastin
- Abstract summary: Human action recognition is an important application domain in computer vision.
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.
- Score: 4.976815699476327
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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