Cider: Enabling Robustness Power Tradeoffs on a Computational Eyeglass
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a paper from MobiCom 2015
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Cider: Enabling Robustness Power Tradeoffs on a Computational Eyeglass
- 1. CIDER enabling robustness-power tradeoffs on a computational eyeglass B00902069 傅蕎
- 3. CIDER • Wearable eye tracker • Power-robustness tradeoff 3
- 5. Tradeoffs • Power • Sensing • Computation • Communication • Robustness • Estimation accuracy • Different illumination conditions (indoor / outdoor) • Zero affect from users 5
- 7. System design • Search-refine controller • Indoor-outdoor model switching • Zero-effort model training 7
- 8. System design • Search-refine controller • Indoor-outdoor model switching • Zero-effort model training 8
- 9. Search-refine controller - search • Artificial neural network (ANN) • Input image, output pupil center coordinate and radius • Objective: minimize sampled pixels minimize error in pupil center prediction • Optimization solved offline in advanced 9
- 10. Search-refine controller - refine • Tracking pupil center and size • Refine the estimation of ANN 10
- 11. Search-refine controller - switching • Only fail when blinks, specular reflections, shadows, or other artifacts 11
- 12. Search-refine controller - NIR • Near infrared LED • Key enabler of rapid switching • High power consumption • Duty-cycle • Reduce voltage • From 180mW to milliwatt 12
- 13. System design • Search-refine controller • Indoor-outdoor model switching • Zero-effort model training 13
- 14. Indoor-outdoor model switching • NIR photodiode • Indoor • NIR useful • more power-saving • Outdoor • NIR unuseful, too much ambient IR • more computation • Negligible switching power and time cost 14
- 15. System design • Search-refine controller • Indoor-outdoor model switching • Zero-effort model training 15
- 16. Zero-effort model training • Robust offline method for generating labels from noisy images • Challenge • Specular reflection of NIR LED • Sun cause glasses shadow when outdoor 16
- 17. Evaluation • 4 data set • Indoor-stable, indoor-variable, outdoor, switching • Cost metrics • Sensing, computation, NIR • Accuracy metrics • Pupil center, pupil radius 17
- 18. Evaluation • Sensing, computation and NIR cost 18
- 20. Evaluation • Power VS tracking rate • Estimation accuracy • CIDER under variable conditions • Variable pupil dilation • Outdoor dateset • Indoor-outdoor switching • Up to 250 ~ 350 Hz eye tracking frame rate 20
- 21. Future work • Dynamics in glasses position • Dynamics in environment • Form-factor • E.g. face shape • Embed the camera in glass frame 21
- 22. Question? 22
- 23. Thank you!:D 23
Editor's Notes
- Sensing pixel/image, high frame rate consume more power -> subsampling, reduce resolution, fewer frame Computation process image to find eye pupil center and dilation -> trim computational requirement VS robustness Communication offload the computation always on-radio consume power, data rate No need train user neural network model automatically retrain when needed
- 2 stage eye tracking controller: search / refine Indoor-outdoor switching model
- 1. Search pixels near pupil center 3. Edge detection to find different part of eye 5. Assume pupil is circle
- Help tracking pupil too Reduce V will cause increased noise
- Camera and ANN parameters need to be adjusted Negligible switching power and time cost
- Challenge Fill the hole, reflection < pupil Find roundest segment