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Human behaviors classification using deep learning technique

Human behaviors is an action performed by human. There are various types of human behaviors such as running, walking, jumping, sitting and the others complex movement. In this paper, human behaviors video-based classification using Long Short Term Memory (LSTM) model with multiple layers were propos...

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Main Authors: Shun, Cheang Chi, Mohd Zamri, Ibrahim, Ikhwan Hafiz, Muhamad
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39582/1/Human%20Behaviors%20Classification%20Using%20Deep%20Learning%20Technique.pdf
http://umpir.ump.edu.my/id/eprint/39582/2/Human%20Behaviors%20Classification%20using%20deep%20learning%20technique_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39582/
https://doi.org/10.1007/978-981-16-8690-0_76
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spelling my.ump.umpir.395822023-12-11T03:39:19Z http://umpir.ump.edu.my/id/eprint/39582/ Human behaviors classification using deep learning technique Shun, Cheang Chi Mohd Zamri, Ibrahim Ikhwan Hafiz, Muhamad T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Human behaviors is an action performed by human. There are various types of human behaviors such as running, walking, jumping, sitting and the others complex movement. In this paper, human behaviors video-based classification using Long Short Term Memory (LSTM) model with multiple layers were proposed to classify the human behaviors. A pre-trained pose estimation model, OpenPose was used to extract the body key points from the Berkeley Multimodal Human Action Database, MHAD database. Six activities, jumping, jumping jacks, punching, waving with two hands, waving with right hand and clapping hands of MHAD database were used for the training and testing. The individual frame of MHAD database will group into 32 window width. Dataset had been increased by creating the 26 of 32 frame overlapping. The performance of 2 layers LSTM model, 3 layers LSTM model, 4 layers LSTM model without dropout layers and 4 layers LSTM model with dropout layers were evaluated and compared. Result shows that 4 layers LSTM model with dropout layers had better performance as compared to 2 layers LSTM model, 3 layers LSTM model and 4 layers LSTM model without dropout layers reached the testing accuracy of 95.86%. With adding of dropout layers in the LSTM model with 4 layers, generalization performance in training process had been increased. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39582/1/Human%20Behaviors%20Classification%20Using%20Deep%20Learning%20Technique.pdf pdf en http://umpir.ump.edu.my/id/eprint/39582/2/Human%20Behaviors%20Classification%20using%20deep%20learning%20technique_ABS.pdf Shun, Cheang Chi and Mohd Zamri, Ibrahim and Ikhwan Hafiz, Muhamad (2022) Human behaviors classification using deep learning technique. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021 , 23 August 2021 , Kuantan, Pahang. pp. 867-881., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 https://doi.org/10.1007/978-981-16-8690-0_76
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Shun, Cheang Chi
Mohd Zamri, Ibrahim
Ikhwan Hafiz, Muhamad
Human behaviors classification using deep learning technique
description Human behaviors is an action performed by human. There are various types of human behaviors such as running, walking, jumping, sitting and the others complex movement. In this paper, human behaviors video-based classification using Long Short Term Memory (LSTM) model with multiple layers were proposed to classify the human behaviors. A pre-trained pose estimation model, OpenPose was used to extract the body key points from the Berkeley Multimodal Human Action Database, MHAD database. Six activities, jumping, jumping jacks, punching, waving with two hands, waving with right hand and clapping hands of MHAD database were used for the training and testing. The individual frame of MHAD database will group into 32 window width. Dataset had been increased by creating the 26 of 32 frame overlapping. The performance of 2 layers LSTM model, 3 layers LSTM model, 4 layers LSTM model without dropout layers and 4 layers LSTM model with dropout layers were evaluated and compared. Result shows that 4 layers LSTM model with dropout layers had better performance as compared to 2 layers LSTM model, 3 layers LSTM model and 4 layers LSTM model without dropout layers reached the testing accuracy of 95.86%. With adding of dropout layers in the LSTM model with 4 layers, generalization performance in training process had been increased.
format Conference or Workshop Item
author Shun, Cheang Chi
Mohd Zamri, Ibrahim
Ikhwan Hafiz, Muhamad
author_facet Shun, Cheang Chi
Mohd Zamri, Ibrahim
Ikhwan Hafiz, Muhamad
author_sort Shun, Cheang Chi
title Human behaviors classification using deep learning technique
title_short Human behaviors classification using deep learning technique
title_full Human behaviors classification using deep learning technique
title_fullStr Human behaviors classification using deep learning technique
title_full_unstemmed Human behaviors classification using deep learning technique
title_sort human behaviors classification using deep learning technique
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39582/1/Human%20Behaviors%20Classification%20Using%20Deep%20Learning%20Technique.pdf
http://umpir.ump.edu.my/id/eprint/39582/2/Human%20Behaviors%20Classification%20using%20deep%20learning%20technique_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39582/
https://doi.org/10.1007/978-981-16-8690-0_76
_version_ 1822923974205505536
score 13.232414

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