# Robust and customized methods for real-time hand gesture recognition under object-occlusion

arXiv: https://arxiv.org/abs/1809.05911 · HTML: https://mlsanity.com/paper/1809.05911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [DiG-Net: Enhancing Human-Robot Interaction through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics](https://mlsanity.com/paper/2505.24786/) (2025)

## Abstract (arXiv)

Dynamic hand tracking and gesture recognition is a hard task since there are many joints on the fingers and each joint owns many degrees of freedom. Besides, object occlusion is also a thorny issue in finger tracking and posture recognition. Therefore, we propose a robust and customized system for realtime hand tracking and gesture recognition under occlusion environment. First, we model the angles between hand keypoints and encode their relative coordinate vectors, then we introduce GAN to generate raw discrete sequence dataset. Secondly we propose a time series forecasting method in the prediction of defined hand keypoint location. Finally, we define a sliding window matching method to complete gesture recognition. We analyze 11 kinds of typical gestures and show how to perform gesture recognition with the proposed method. Our work can reach state of the art results and contribute to build a framework to implement customized gesture recognition task.