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Duo Streamers: A Streaming Gesture Recognition Framework

arXiv 2502.12297 ↗First posted 17 Feb 2025cs.CVcited by 0

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Abstract (arXiv)

Gesture recognition in resource-constrained scenarios faces significant challenges in achieving high accuracy and low latency. The streaming gesture recognition framework, Duo Streamers, proposed in this paper, addresses these challenges through a three-stage sparse recognition mechanism, an RNN-lite model with an external hidden state, and specialized training and post-processing pipelines, thereby making innovative progress in real-time performance and lightweight design. Experimental results show that Duo Streamers matches mainstream methods in accuracy metrics, while reducing the real-time factor by approximately 92.3%, i.e., delivering a nearly 13-fold speedup. In addition, the framework shrinks parameter counts to 1/38 (idle state) and 1/9 (busy state) compared to mainstream models. In summary, Duo Streamers not only offers an efficient and practical solution for streaming gesture recognition in resource-constrained devices but also lays a solid foundation for extended applications in multimodal and diverse scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 17 Feb 2025.

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References · 17

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  10. 2024An Advanced Deep Learning Based Three-Stream Hybrid Model for Dynamic Hand Gesture Recognitioncited by 4
  11. 2024Towards Open-World Gesture Recognitioncited by 3
  12. 2024Forearm Ultrasound based Gesture Recognition on Edgecited by 3
5 more
  1. 2023BAT: Boundary aware transducer for memory-efficient and low-latency ASRcited by 2
  2. 2024Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNcited by 2
  3. 2024RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Frameworkcited by 2
  4. 2020Gesture Recognition from Skeleton Data for Intuitive Human-Machine Interactioncited by 1
  5. 2024Boosting Gesture Recognition with an Automatic Gesture Annotation Frameworkcited by 1

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