Using Depth Cameras for Recognition and Segmentation of Hand Gestures
In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/7100727 |
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| author | Khalid Twarish Alhamazani Jalawi Alshudukhi Talal Saad Alharbi Saud Aljaloud Zelalem Meraf |
| author_facet | Khalid Twarish Alhamazani Jalawi Alshudukhi Talal Saad Alharbi Saud Aljaloud Zelalem Meraf |
| author_sort | Khalid Twarish Alhamazani |
| collection | DOAJ |
| description | In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions. |
| format | Article |
| id | doaj-art-2f67d5f05b294e789f93da7b971031dc |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-2f67d5f05b294e789f93da7b971031dc2025-02-03T07:24:15ZengWileyAdvances in Materials Science and Engineering1687-84422021-01-01202110.1155/2021/7100727Using Depth Cameras for Recognition and Segmentation of Hand GesturesKhalid Twarish Alhamazani0Jalawi Alshudukhi1Talal Saad Alharbi2Saud Aljaloud3Zelalem Meraf4University of Ha’il College of Computer Science and EngineeringUniversity of Ha’il College of Computer Science and EngineeringUniversity of Ha’il College of Computer Science and EngineeringUniversity of Ha’il College of Computer Science and EngineeringDepartment of StatisticsIn recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions.http://dx.doi.org/10.1155/2021/7100727 |
| spellingShingle | Khalid Twarish Alhamazani Jalawi Alshudukhi Talal Saad Alharbi Saud Aljaloud Zelalem Meraf Using Depth Cameras for Recognition and Segmentation of Hand Gestures Advances in Materials Science and Engineering |
| title | Using Depth Cameras for Recognition and Segmentation of Hand Gestures |
| title_full | Using Depth Cameras for Recognition and Segmentation of Hand Gestures |
| title_fullStr | Using Depth Cameras for Recognition and Segmentation of Hand Gestures |
| title_full_unstemmed | Using Depth Cameras for Recognition and Segmentation of Hand Gestures |
| title_short | Using Depth Cameras for Recognition and Segmentation of Hand Gestures |
| title_sort | using depth cameras for recognition and segmentation of hand gestures |
| url | http://dx.doi.org/10.1155/2021/7100727 |
| work_keys_str_mv | AT khalidtwarishalhamazani usingdepthcamerasforrecognitionandsegmentationofhandgestures AT jalawialshudukhi usingdepthcamerasforrecognitionandsegmentationofhandgestures AT talalsaadalharbi usingdepthcamerasforrecognitionandsegmentationofhandgestures AT saudaljaloud usingdepthcamerasforrecognitionandsegmentationofhandgestures AT zelalemmeraf usingdepthcamerasforrecognitionandsegmentationofhandgestures |