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Human activity detection and action recognition in video using convolutional neural networks

Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging problems in the area of video analysis and it finds applications in human-computer interaction, anomalous activity de...

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Main Authors: Basavaiah, Jagadeesh, Patil, Chandrashekar Mohan
Format: Article
Language:English
Published: Universiti Utara Malaysia 2020
Subjects:
QA76 Computer software
Online Access:http://repo.uum.edu.my/26967/1/JICT%2019%202%202020%20157-183.pdf
http://repo.uum.edu.my/26967/
http://jict.uum.edu.my/index.php/currentissues#a1
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spelling my.uum.repo.269672020-04-30T03:08:58Z http://repo.uum.edu.my/26967/ Human activity detection and action recognition in video using convolutional neural networks Basavaiah, Jagadeesh Patil, Chandrashekar Mohan QA76 Computer software Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging problems in the area of video analysis and it finds applications in human-computer interaction, anomalous activity detection, crowd monitoring and patient monitoring. Several approaches have been presented for human activity recognition using machine learning techniques. The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation. Tracking of human activity in the video is implemented using the Gaussian Mixture Model. Convolutional Neural Network based classification approach is used for database training and testing purposes. The activity recognition performance is evaluated for two public datasets namely Weizmann dataset and Kungliga Tekniska Hogskolan dataset with action recognition accuracy of 98.43% and 94.96%, respectively. Experimental and comparative studies have shown that the proposed approach outperformed state-of the art techniques. Universiti Utara Malaysia 2020-04 Article PeerReviewed application/pdf en http://repo.uum.edu.my/26967/1/JICT%2019%202%202020%20157-183.pdf Basavaiah, Jagadeesh and Patil, Chandrashekar Mohan (2020) Human activity detection and action recognition in video using convolutional neural networks. Journal of Information and Communication Technology (JICT), 19 (2). pp. 157-183. ISSN 1675-414X http://jict.uum.edu.my/index.php/currentissues#a1
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Basavaiah, Jagadeesh
Patil, Chandrashekar Mohan
Human activity detection and action recognition in video using convolutional neural networks
description Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging problems in the area of video analysis and it finds applications in human-computer interaction, anomalous activity detection, crowd monitoring and patient monitoring. Several approaches have been presented for human activity recognition using machine learning techniques. The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation. Tracking of human activity in the video is implemented using the Gaussian Mixture Model. Convolutional Neural Network based classification approach is used for database training and testing purposes. The activity recognition performance is evaluated for two public datasets namely Weizmann dataset and Kungliga Tekniska Hogskolan dataset with action recognition accuracy of 98.43% and 94.96%, respectively. Experimental and comparative studies have shown that the proposed approach outperformed state-of the art techniques.
format Article
author Basavaiah, Jagadeesh
Patil, Chandrashekar Mohan
author_facet Basavaiah, Jagadeesh
Patil, Chandrashekar Mohan
author_sort Basavaiah, Jagadeesh
title Human activity detection and action recognition in video using convolutional neural networks
title_short Human activity detection and action recognition in video using convolutional neural networks
title_full Human activity detection and action recognition in video using convolutional neural networks
title_fullStr Human activity detection and action recognition in video using convolutional neural networks
title_full_unstemmed Human activity detection and action recognition in video using convolutional neural networks
title_sort human activity detection and action recognition in video using convolutional neural networks
publisher Universiti Utara Malaysia
publishDate 2020
url http://repo.uum.edu.my/26967/1/JICT%2019%202%202020%20157-183.pdf
http://repo.uum.edu.my/26967/
http://jict.uum.edu.my/index.php/currentissues#a1
_version_ 1665896908637339648
score 13.211869

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