2017/12/13 by Rohit Pandey, Pandey, Rohit, Marie White +8 · 2 citations
Computer Science · Psychology · #Artificial intelligence #Bounding overwatch #Computer Vision and Pattern Recognition (cs.CV) #Computer graphics (images) #Computer science #Computer vision #FOS: Computer and information sciences #Gesture #Gesture recognition #Hand Gesture Recognition Systems #Head (geology) #Hearing Impairment and Communication #Human Pose and Action Recognition #Image (mathematics) #Minimum bounding box #Mobile device #Virtual reality #cs.CV
paper · pdf · doi:10.48550/arxiv.1712.04961
published in arXiv (Cornell University) (Cornell University) · Extended Abstract NIPS 2017 Machine Learning on the Phone and other Consumer Devices Workshop
arxiv created 2017/12/13 · openalex publication_date 2017/12/13 · arxiv updated 2017/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mobile virtual reality (VR) head mounted displays (HMD) have become popular among consumers in recent years. In this work, we demonstrate real-time egocentric hand gesture detection and localization on mobile HMDs. Our main contributions are: 1) A novel mixed-reality data collection tool to automatic annotate bounding boxes and gesture labels; 2) The largest-to-date egocentric hand gesture and bounding box dataset with more than 400,000 annotated frames; 3) A neural network that runs real time on modern mobile CPUs, and achieves higher than 76% precision on gesture recognition across 8 classes.