Human Action Recognition Using Difference of Gaussian and Difference of Wavelet
Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent sp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020040 |
| _version_ | 1797840422903480320 |
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| author | Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui |
| author_facet | Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui |
| author_sort | Gopampallikar Vinoda Reddy |
| collection | DOAJ |
| description | Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy. |
| first_indexed | 2024-04-09T16:14:52Z |
| format | Article |
| id | doaj.art-98695e8ba5fc45c498d34a4733a1fed4 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-09T16:14:52Z |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-98695e8ba5fc45c498d34a4733a1fed42023-04-24T06:02:31ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016333634610.26599/BDMA.2022.9020040Human Action Recognition Using Difference of Gaussian and Difference of WaveletGopampallikar Vinoda Reddy0Kongara Deepika1Lakshmanan Malliga2Duraivelu Hemanand3Chinnadurai Senthilkumar4Subburayalu Gopalakrishnan5Yousef Farhaoui6Department of Computer Science and Engineering (AI&ML), CMR Technical Campus, Hyderabad 501401, India.Department of Information Technology, Kakatiya Institute of Technology and Science, Warangal 506015, India.Department of Electronics and Communication Engineering, Malla Reddy Engineering College for Women (Autonomous), Hyderabad 500100, India.Department of Computer Science and Engineering, S. A. Engineering College (Autonomous), Thiruverkadu 600077, India.Department of Electronics and Communication Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, India.Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.IDMS Team, STI Laboratory, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 25003, Morocco.Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy.https://www.sciopen.com/article/10.26599/BDMA.2022.9020040human action recognitiondifference of gaussiandifference of waveletlinear discriminant analysisweizmannucf 11accuracy |
| spellingShingle | Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Human Action Recognition Using Difference of Gaussian and Difference of Wavelet Big Data Mining and Analytics human action recognition difference of gaussian difference of wavelet linear discriminant analysis weizmann ucf 11 accuracy |
| title | Human Action Recognition Using Difference of Gaussian and Difference of Wavelet |
| title_full | Human Action Recognition Using Difference of Gaussian and Difference of Wavelet |
| title_fullStr | Human Action Recognition Using Difference of Gaussian and Difference of Wavelet |
| title_full_unstemmed | Human Action Recognition Using Difference of Gaussian and Difference of Wavelet |
| title_short | Human Action Recognition Using Difference of Gaussian and Difference of Wavelet |
| title_sort | human action recognition using difference of gaussian and difference of wavelet |
| topic | human action recognition difference of gaussian difference of wavelet linear discriminant analysis weizmann ucf 11 accuracy |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020040 |
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