Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex model...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2745 |
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| author | Najeeb ur Rehman Malik Usman Ullah Sheikh Syed Abdul Rahman Abu-Bakar Asma Channa |
| author_facet | Najeeb ur Rehman Malik Usman Ullah Sheikh Syed Abdul Rahman Abu-Bakar Asma Channa |
| author_sort | Najeeb ur Rehman Malik |
| collection | DOAJ |
| description | Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique. |
| first_indexed | 2024-03-11T07:10:42Z |
| format | Article |
| id | doaj.art-ac85bba44f0249b2889c89734d8d778f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:42Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ac85bba44f0249b2889c89734d8d778f2023-11-17T08:39:11ZengMDPI AGSensors1424-82202023-03-01235274510.3390/s23052745Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping TechniqueNajeeb ur Rehman Malik0Usman Ullah Sheikh1Syed Abdul Rahman Abu-Bakar2Asma Channa3Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaComputer Science Department, University Politehnica of Bucharest, 060042 Bucharest, RomaniaHuman action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.https://www.mdpi.com/1424-8220/23/5/2745HARskeletonOpenPoseMLFineKNNEFS |
| spellingShingle | Najeeb ur Rehman Malik Usman Ullah Sheikh Syed Abdul Rahman Abu-Bakar Asma Channa Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique Sensors HAR skeleton OpenPose ML FineKNN EFS |
| title | Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique |
| title_full | Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique |
| title_fullStr | Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique |
| title_full_unstemmed | Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique |
| title_short | Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique |
| title_sort | multi view human action recognition using skeleton based fineknn with extraneous frame scrapping technique |
| topic | HAR skeleton OpenPose ML FineKNN EFS |
| url | https://www.mdpi.com/1424-8220/23/5/2745 |
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