HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS 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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Autores principales:	Jagadeesh Basavaiah, Chandrashekar Mohan Patil
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	action recognition convolutional neural network gaussian mixture model optical flow sift feature extraction Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/c864a20c77774856b83d624490f79aaa
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spelling	oai:doaj.org-article:c864a20c77774856b83d624490f79aaa2021-11-11T03:40:14ZHUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS10.32890/jict2020.19.2.11675-414X2180-3862https://doaj.org/article/c864a20c77774856b83d624490f79aaa2020-03-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.2.1https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Human 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. Jagadeesh BasavaiahChandrashekar Mohan PatilUUM Pressarticleaction recognitionconvolutional neural networkgaussian mixture modeloptical flowsift feature extractionInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 2, Pp 157-183 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	action recognition convolutional neural network gaussian mixture model optical flow sift feature extraction Information technology T58.5-58.64
spellingShingle	action recognition convolutional neural network gaussian mixture model optical flow sift feature extraction Information technology T58.5-58.64 Jagadeesh Basavaiah Chandrashekar Mohan Patil HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS 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	Jagadeesh Basavaiah Chandrashekar Mohan Patil
author_facet	Jagadeesh Basavaiah Chandrashekar Mohan Patil
author_sort	Jagadeesh Basavaiah
title	HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
title_short	HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
title_full	HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
title_fullStr	HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
title_full_unstemmed	HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
title_sort	human activity detection and action recognition in videos using convolutional neural networks
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/c864a20c77774856b83d624490f79aaa
work_keys_str_mv	AT jagadeeshbasavaiah humanactivitydetectionandactionrecognitioninvideosusingconvolutionalneuralnetworks AT chandrashekarmohanpatil humanactivitydetectionandactionrecognitioninvideosusingconvolutionalneuralnetworks
_version_	1718439627323342848

HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS

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