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

2019/01/11 by Hai-Duong Nguyen, Duong Hai Nguyen, Nguyen, Duong Hai +7
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Robot Manipulation and Learning #cs.CV

paper · pdf · doi:10.48550/arxiv.1901.03465

7 pages, 10 figures

openalex publication_date 2019/01/11 · openalex created_date 2019/01/25 · arxiv created 2020/03/11 · arxiv updated 2020/03/12 · openalex updated_date 2026/07/28

Abstract

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.

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