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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bulling, Andreas Dodgson, Neil Świrski, Lech |
| Abstract | Robust, accurate, real-time pupil tracking is a key component for online gaze estimation. On head-mounted eye trackers, existing algorithms that rely on circular pupils or contiguous pupil regions fail to detect or accurately track the pupil. This is because the pupil ellipse is often highly eccentric and partially occluded by eyelashes. We present a novel, real-time dark-pupil tracking algorithm that is robust under such conditions. Our approach uses a Haar-like feature detector to roughly estimate the pupil location, performs a k-means segmentation on the surrounding region to refine the pupil centre, and fits an ellipse to the pupil using a novel image-aware Random Sample Concensus (RANSAC) ellipse fitting. We compare our approach against existing real-time pupil tracking implementations, using a set of manually labelled infra-red dark-pupil eye images. We show that our technique has a higher pupil detection rate and greater pupil tracking accuracy. |
| Starting Page | 173 |
| Ending Page | 176 |
| Page Count | 4 |
| File Format | PDF MP4 |
| ISBN | 9781450312219 |
| DOI | 10.1145/2168556.2168585 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-03-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Audio Text |
| Resource Type | Article |
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