Recognition of human action for scene understanding using world cup optimization and transfer learning approach
Understanding human activities is one of the vital steps in visual scene recognition. Human daily activities include diverse scenes with multiple objects having complex interrelationships with each other. Representation of human activities finds application in areas such as surveillance, health care...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1396.pdf |
| _version_ | 1797820221381148672 |
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| author | Ranjini Surendran Anitha J Jude D. Hemanth |
| author_facet | Ranjini Surendran Anitha J Jude D. Hemanth |
| author_sort | Ranjini Surendran |
| collection | DOAJ |
| description | Understanding human activities is one of the vital steps in visual scene recognition. Human daily activities include diverse scenes with multiple objects having complex interrelationships with each other. Representation of human activities finds application in areas such as surveillance, health care systems, entertainment, automated patient monitoring systems, and so on. Our work focuses on classifying scenes into different classes of human activities like waving hands, gardening, walking, running, etc. The dataset classes were pre-processed using the fuzzy color stacking technique. We adopted the transfer learning concept of pretrained deep CNN models. Our proposed methodology employs pretrained AlexNet, SqueezeNet, ResNet, and DenseNet for feature extraction. The adaptive World Cup Optimization (WCO) algorithm is used halfway to select the superior dominant features. Then, these dominant features are classified by the fully connected classifier layer of DenseNet 201. Evaluation of the performance matrices showed an accuracy of 94.7% with DenseNet as the feature extractor and WCO for feature selection compared to other models. Also, our proposed methodology proved to be superior to its counterpart without feature selection. Thus, we could improve the quality of the classification model by providing double filtering using the WCO feature selection process. |
| first_indexed | 2024-03-13T09:35:14Z |
| format | Article |
| id | doaj.art-2fe8b06ac12c4391a5436104bd5d31b5 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-13T09:35:14Z |
| publishDate | 2023-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-2fe8b06ac12c4391a5436104bd5d31b52023-05-25T15:05:08ZengPeerJ Inc.PeerJ Computer Science2376-59922023-05-019e139610.7717/peerj-cs.1396Recognition of human action for scene understanding using world cup optimization and transfer learning approachRanjini Surendran0Anitha J1Jude D. Hemanth2Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, IndiaDepartment of ECE, Karunya Institute of Technology and Sciences, Coimbatore, IndiaDepartment of ECE, Karunya Institute of Technology and Sciences, Coimbatore, IndiaUnderstanding human activities is one of the vital steps in visual scene recognition. Human daily activities include diverse scenes with multiple objects having complex interrelationships with each other. Representation of human activities finds application in areas such as surveillance, health care systems, entertainment, automated patient monitoring systems, and so on. Our work focuses on classifying scenes into different classes of human activities like waving hands, gardening, walking, running, etc. The dataset classes were pre-processed using the fuzzy color stacking technique. We adopted the transfer learning concept of pretrained deep CNN models. Our proposed methodology employs pretrained AlexNet, SqueezeNet, ResNet, and DenseNet for feature extraction. The adaptive World Cup Optimization (WCO) algorithm is used halfway to select the superior dominant features. Then, these dominant features are classified by the fully connected classifier layer of DenseNet 201. Evaluation of the performance matrices showed an accuracy of 94.7% with DenseNet as the feature extractor and WCO for feature selection compared to other models. Also, our proposed methodology proved to be superior to its counterpart without feature selection. Thus, we could improve the quality of the classification model by providing double filtering using the WCO feature selection process.https://peerj.com/articles/cs-1396.pdfDeep learningConvolutional neural networkTransfer learningWorld cup optimization |
| spellingShingle | Ranjini Surendran Anitha J Jude D. Hemanth Recognition of human action for scene understanding using world cup optimization and transfer learning approach PeerJ Computer Science Deep learning Convolutional neural network Transfer learning World cup optimization |
| title | Recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| title_full | Recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| title_fullStr | Recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| title_full_unstemmed | Recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| title_short | Recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| title_sort | recognition of human action for scene understanding using world cup optimization and transfer learning approach |
| topic | Deep learning Convolutional neural network Transfer learning World cup optimization |
| url | https://peerj.com/articles/cs-1396.pdf |
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