2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | CI-3.5 | ||
| Paper Title | A HIGH-FRAME-RATE EYE-TRACKING FRAMEWORK FOR MOBILE DEVICES | ||
| Authors | Yuhu Chang, Changyang He, Yingying Zhao, Tun Lu, Ning Gu, Fudan University, China | ||
| Session | CI-3: Computational Photography | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 15:30 - 16:15 | ||
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 | ||
| Presentation | Poster | ||
| Topic | Computational Imaging: [CIS] Computational Imaging Systems | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Gaze-on-screen tracking, an appearance-based eye-tracking task, has drawn significant interest in recent years. While learning-based high-precision eye-tracking methods have been designed in the past, the complex pre-training and high computation in neural network-based deep models restrict their applicability in mobile devices. Moreover, as the display frame rate of mobile devices has steadily increased to 120 fps, high-frame-rate eye tracking becomes increasingly challenging. In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. Specifically, GazeHFR classifies the eye movement into two distinct phases, i.e., saccade and smooth pursuit, and leverages inter-frame motion information combined with lightweight learning models tailored to each movement phase to deliver high-efficient eye tracking without affecting accuracy. Compared to prior art, GazeHFR achieves approximately 7x speedup and 15% accuracy improvement on mobile devices. | ||