A 124.9fps memory-efficient hand segmentation processor for hand gesture in mobile devices

Abstract

Hand gesture recognition is one of emerging Human Computer Interaction (HCI) technologies for the next generation of mobile devices. However, conventional software-oriented approaches spend a considerable time and require a large memory size for hand segmentation, which fails to give real-time interactions between users and mobile devices. Therefore, in this paper, we present a high-throughput and memory-efficient hand segmentation processor. To obtain both of high throughput and high memory-efficiency, we propose a parallelized hand candidate decision and a compressed feedback histogram. As a result, it achieves 124.9 fps with only 26.9 KB on-chip memory, which are 1.39 times faster and 92 time smaller, respectively, compared to the state-of-the-art.