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Online Hand Gesture Recognition Using 3D Convolutional Neural Networks

arXiv 2605.23409 ↗First posted 22 May 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 22 May 2026.

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References · 11

  1. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  2. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  3. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 899
  4. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  5. 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 483
  6. 2017Temporal Relational Reasoning in Videoscited by 324
  7. 2017Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitioncited by 178
  8. 2019Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networkscited by 30
  9. 2018Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Trainingcited by 28
  10. 2015A robust and efficient video representation for action recognitioncited by 20
  11. 2018Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognitioncited by 15

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