# Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet

arXiv: https://arxiv.org/abs/1901.03465 · HTML: https://mlsanity.com/paper/1901.03465/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection](https://mlsanity.com/paper/2101.02047/) (2021)
- [Color Invariant Skin Segmentation](https://mlsanity.com/paper/2204.09882/) (2022)
- [Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks](https://mlsanity.com/paper/2205.06980/) (2022)

## Abstract (arXiv)

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.