Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and enhanced visual privacy. However, existing eye tracking systems are still limited by their: (1) large form-factor largely due to the adopted bulky lens-based cameras; (2) high communication cost required between the camera and backend processor; and (3) potentially concerned low visual privacy, thus prohibiting their more extensive applications. To this end, we propose, develop, and validate a lensless FlatCambased eye tracking algorithm and accelerator co-design framework dubbed EyeCoD to enable eye tracking systems with a much reduced form-factor and boosted system efficiency without sacrificing the tracking accuracy, paving the way for next-generation eye tracking solutions. On the system level, we advocate the use of lensless FlatCams instead of lens-based cameras to facilitate the small form-factor need in mobile eye tracking systems, which also leaves rooms for a dedicated sensing-processor co-design to reduce the required camera-processor communication latency. On the algorithm level, EyeCoD integrates a predict-then-focus pipeline that first predicts the region-of-interest (ROI) via segmentation and then only focuses on the ROI parts to estimate gaze directions, greatly reducing redundant computations and data movements. On the hardware level, we further develop a dedicated accelerator that (1) integrates a novel workload orchestration between the aforementioned segmentation and gaze estimation models, (2) leverages intra-channel reuse opportunities for depth-wise layers, (3) utilizes input feature-wise partition to save activation memory size, and (4) develops a sequential-write-parallel-read input buffer to alleviate the bandwidth requirement for the activation global buffer. On-silicon measurement and extensive experiments validate that our EyeCoD consistently reduces both the communication and computation costs, leading to an overall system speedup of 10.95×, 3.21×, and 12.85× over general computing platforms including CPUs and GPUs, and a prior-art eye tracking processor called CIS-GEP, respectively, while maintaining the tracking accuracy. Codes are available at https://github.com/RICE-EIC/EyeCoD.
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Estimating Perceptual Depth Changes with Eye Vergence and Interpupillary Distance using an Eye Tracker in Virtual Reality
Virtual Reality (VR) technology has advanced to include eye-tracking, allowing novel research, such as investigating how our visual system coordinates eye movements with changes in perceptual depth. The purpose of this study was to examine whether eye tracking could track perceptual depth changes during a visual discrimination task. We derived two depth-dependent variables from eye tracker data: eye vergence angle (EVA) and interpupillary distance (IPD). As hypothesized, our results revealed that shifting gaze from near-to-far depth significantly decreased EVA and increased IPD, while the opposite pattern was observed while shifting from far-to-near. Importantly, the amount of change in these variables tracked closely with relative changes in perceptual depth, and supported the hypothesis that eye tracker data may be used to infer real-time changes in perceptual depth in VR. Our method could be used as a new tool to adaptively render information based on depth and improve the VR user experience.
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- Award ID(s):
- 1937565
- NSF-PAR ID:
- 10390194
- Editor(s):
- Blascheck, Tanja; Bradshaw, Jessica; Vrzakova, Hana
- Date Published:
- Journal Name:
- ACM Symposium on Eye Tracking Research and Applications
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3517031.3529632
