# Recognition of Dynamic Hand Gestures in Long Distance using a Web-Camera for Robot Guidance

arXiv: https://arxiv.org/abs/2406.12424 · HTML: https://mlsanity.com/paper/2406.12424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction](https://mlsanity.com/paper/2311.15361/) (2023)
- [Real-time Human Finger Pointing Recognition and Estimation for Robot Directives Using a Single Web-Camera](https://mlsanity.com/paper/2307.02949/) (2023)

## Abstract (arXiv)

Dynamic gestures enable the transfer of directive information to a robot. Moreover, the ability of a robot to recognize them from a long distance makes communication more effective and practical. However, current state-of-the-art models for dynamic gestures exhibit limitations in recognition distance, typically achieving effective performance only within a few meters. In this work, we propose a model for recognizing dynamic gestures from a long distance of up to 20 meters. The model integrates the SlowFast and Transformer architectures (SFT) to effectively process and classify complex gesture sequences captured in video frames. SFT demonstrates superior performance over existing models.