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arXiv · 2502.12297

Duo Streamers: A Streaming Gesture Recognition Framework

Boxuan Zhu·Sicheng Yang·Zhuo Wang·Haining Liang·Junxiao Shen
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Abstract

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.

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Boxuan Zhu, Sicheng Yang, Zhuo Wang, Haining Liang, Junxiao Shen. 2025-02-17. Duo Streamers: A Streaming Gesture Recognition Framework. https://arxiv.org/abs/2502.12297

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