Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet
This work addresses hand gesture recognition for human-machine interaction, but it is incremental as it builds on existing methods like SegNet.
The paper tackled hand segmentation and fingertip detection from RGB-D images for gesture-based interaction, proposing a modified SegNet architecture that achieves comparable performance with low model complexity suitable for real-time applications.
Hand segmentation and fingertip detection play an indispensable role in hand gesture-based human-machine interaction systems. In this study, we propose a method to discriminate hand components and to locate fingertips in RGB-D images. The system consists of three main steps: hand detection using RGB images providing regions which are considered as promising areas for further processing, hand segmentation, and fingertip detection using depth image and our modified SegNet, a single lightweight architecture that can process two independent tasks at the same time. The experimental results show that our system is a promising method for hand segmentation and fingertip detection which achieves a comparable performance while model complexity is suitable for real-time applications.