Online Hand Gesture Recognition Using 3D Convolutional Neural Networks
A new system for online hand gesture recognition using 3D convolutional neural networks has been proposed. This system is designed to detect and classify dynamic hand gestures in real-time video streams with minimal lag. The models trained on the Jester database achieved high accuracy rates, demonstrating the effectiveness of the approach.
- ▪The proposed system localizes and recognizes hand gestures in real-time video streams.
- ▪It utilizes a sliding window approach to enhance the robustness of gesture recognition.
- ▪Models trained on the Jester database achieved over 98% accuracy for detection and over 90% for classification.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.23409 (cs) [Submitted on 22 May 2026] Title:Online Hand Gesture Recognition Using 3D Convolutional Neural Networks Authors:Yinghao Qin, Tijana Timotijevic View a PDF of the paper titled Online Hand Gesture Recognition Using 3D Convolutional Neural Networks, by Yinghao Qin and 1 other authors View PDF HTML (experimental) Abstract: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.
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