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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| Materialtyp: | Conference or Workshop Item |
| Språk: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Länkar: | 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 |
| _version_ | 1825815322185170944 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T13:11:47Z |
| format | Conference or Workshop Item |
| id | UMPir39582 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:11:47Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir395822023-12-11T03:39:19Z 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 (Published) https://doi.org/10.1007/978-981-16-8690-0_76 |
| 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 |
| title | 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_short | Human behaviors classification using deep learning technique |
| title_sort | human behaviors classification using deep learning technique |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| 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 |
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