Online Hand Gesture Recognition Using 3D Convolutional Neural Networks
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910247720845312 |
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| author | Qin, Yinghao Timotijevic, Tijana |
| author_facet | Qin, Yinghao Timotijevic, Tijana |
| contents | In human computer interaction, real-time detection and classification of dynamic hand gestures is challenging as: 1) the system must run in a real-time video stream and there is no noticeable lag in response after performing a gesture; 2) there is a large difference in how people perform gestures, making recognition more difficult. In this paper, an online hand gesture recognition system is proposed, which is able to localize gestures in real-time video stream and recognize what these gestures are. To improve the robustness of the system, the sliding window approach is used to refine results from multiple windows. All of the models in my project are trained on Jester database, achieving 98+% accuracy for detector and 90+% accuracy for classifier. For the overall performance of the system, the best group can respond within three seconds and reach 37.5% Levenshtein accuracy on the homemade dataset. The project codes used in this work are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23409 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Online Hand Gesture Recognition Using 3D Convolutional Neural Networks Qin, Yinghao Timotijevic, Tijana Computer Vision and Pattern Recognition Artificial Intelligence In human computer interaction, real-time detection and classification of dynamic hand gestures is challenging as: 1) the system must run in a real-time video stream and there is no noticeable lag in response after performing a gesture; 2) there is a large difference in how people perform gestures, making recognition more difficult. In this paper, an online hand gesture recognition system is proposed, which is able to localize gestures in real-time video stream and recognize what these gestures are. To improve the robustness of the system, the sliding window approach is used to refine results from multiple windows. All of the models in my project are trained on Jester database, achieving 98+% accuracy for detector and 90+% accuracy for classifier. For the overall performance of the system, the best group can respond within three seconds and reach 37.5% Levenshtein accuracy on the homemade dataset. The project codes used in this work are publicly available. |
| title | Online Hand Gesture Recognition Using 3D Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.23409 |