UAV manipulation by hand gesture recognition
In this study, we discuss a unmanned aerial vehicle operation system by recognizing human gestures. Here, we focus on both dynamic and static gestures, such as moving the right hand repeatedly or holding it in a certain position. And, we propose two methods, one is a feature-based (FB) method to det...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-06-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2022.2103631 |
| _version_ | 1827794882930933760 |
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| author | Shoichiro Togo Hiroyuki Ukida |
| author_facet | Shoichiro Togo Hiroyuki Ukida |
| author_sort | Shoichiro Togo |
| collection | DOAJ |
| description | In this study, we discuss a unmanned aerial vehicle operation system by recognizing human gestures. Here, we focus on both dynamic and static gestures, such as moving the right hand repeatedly or holding it in a certain position. And, we propose two methods, one is a feature-based (FB) method to detect the position of the right hand in an image and identify the gesture form features estimated by FFT, and the other is a machine learning (ML) method to detect the position of the right hand in an image and identify the gesture by the framework of the ML. In experiments, we compare the results of gesture recognition by each method. As a result, the recognition rate of the FB method is higher than that of the ML method under the conditions assumed in the FB method. But, in other cases, the ML method is higher than that of the FB method. The ML method is also effective in terms of extensibility, such as adding more types of gestures. |
| first_indexed | 2024-03-11T18:40:11Z |
| format | Article |
| id | doaj.art-d3e3f33fc2714c0baba961e93ab5d568 |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:40:11Z |
| publishDate | 2022-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-d3e3f33fc2714c0baba961e93ab5d5682023-10-12T13:43:52ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702022-06-0115214516110.1080/18824889.2022.21036312103631UAV manipulation by hand gesture recognitionShoichiro Togo0Hiroyuki Ukida1Graduate School of Advanced Technology and Science, Tokushima UniversityGraduate School of Advanced Technology and Science, Tokushima UniversityIn this study, we discuss a unmanned aerial vehicle operation system by recognizing human gestures. Here, we focus on both dynamic and static gestures, such as moving the right hand repeatedly or holding it in a certain position. And, we propose two methods, one is a feature-based (FB) method to detect the position of the right hand in an image and identify the gesture form features estimated by FFT, and the other is a machine learning (ML) method to detect the position of the right hand in an image and identify the gesture by the framework of the ML. In experiments, we compare the results of gesture recognition by each method. As a result, the recognition rate of the FB method is higher than that of the ML method under the conditions assumed in the FB method. But, in other cases, the ML method is higher than that of the FB method. The ML method is also effective in terms of extensibility, such as adding more types of gestures.http://dx.doi.org/10.1080/18824889.2022.2103631gesture recognitionuav manipulationfeature extractionhand region estimationfast fourier transformmachine learningopenposelong short-term memory |
| spellingShingle | Shoichiro Togo Hiroyuki Ukida UAV manipulation by hand gesture recognition SICE Journal of Control, Measurement, and System Integration gesture recognition uav manipulation feature extraction hand region estimation fast fourier transform machine learning openpose long short-term memory |
| title | UAV manipulation by hand gesture recognition |
| title_full | UAV manipulation by hand gesture recognition |
| title_fullStr | UAV manipulation by hand gesture recognition |
| title_full_unstemmed | UAV manipulation by hand gesture recognition |
| title_short | UAV manipulation by hand gesture recognition |
| title_sort | uav manipulation by hand gesture recognition |
| topic | gesture recognition uav manipulation feature extraction hand region estimation fast fourier transform machine learning openpose long short-term memory |
| url | http://dx.doi.org/10.1080/18824889.2022.2103631 |
| work_keys_str_mv | AT shoichirotogo uavmanipulationbyhandgesturerecognition AT hiroyukiukida uavmanipulationbyhandgesturerecognition |