Recent advances in video-based human action recognition using deep learning: A review

Paper

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2017. "Recent advances in video-based human action recognition using deep learning: A review." 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, United States 14 - 19 May 2017 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2017.7966210
Paper/Presentation Title

Recent advances in video-based human action recognition using deep learning: A review

Presentation TypePaper
AuthorsWu, Di, Sharma, Nabin and Blumenstein, Michael
Journal or Proceedings TitleProceedings of 2017 International Joint Conference on Neural Networks (IJCNN)
Journal Citationpp. 2865-2872
Number of Pages8
Year2017
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2161-4407
ISBN9781509061822
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN.2017.7966210
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/7966210
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/7958416/proceeding
Conference/Event2017 International Joint Conference on Neural Networks (IJCNN)
Event Details
2017 International Joint Conference on Neural Networks (IJCNN)
IJCNN
Parent
International Joint Conference on Neural Networks (IJCNN)
Delivery
In person
Event Date
14 to end of 19 May 2017
Event Location
Anchorage, United States
Abstract

Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers.

Keywordsdeep learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
460304. Computer vision
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Byline AffiliationsUniversity of Technology Sydney
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