Duo Streamers: A Streaming Gesture Recognition Framework
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917935826599936 |
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| author | Zhu, Boxuan Yang, Sicheng Wang, Zhuo Liang, Haining Shen, Junxiao |
| author_facet | Zhu, Boxuan Yang, Sicheng Wang, Zhuo Liang, Haining Shen, Junxiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Duo Streamers: A Streaming Gesture Recognition Framework Zhu, Boxuan Yang, Sicheng Wang, Zhuo Liang, Haining Shen, Junxiao Computer Vision and Pattern Recognition 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. |
| title | Duo Streamers: A Streaming Gesture Recognition Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.12297 |