3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in pupil-detection
Use these libraries to find pupil-detection models and implementations
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Pupil is an accessible, affordable, and extensible open source platform for pervasive eye tracking and gaze-based interaction and includes state-of-the-art algorithms for real-time pupil detection and tracking, calibration, and accurate gaze estimation.
A pupil detection method that utilizes the high frame rate of eye tracking cameras to obtain reliable predictions through recursive estimation about certain pupil characteristics in successive camera frames and is found to have a greater detection rate, accuracy and speed compared to other recently published open-source pupil detection algorithms.
The Pu pil Re constructor (PuRe) is introduced, a method for pupil detection in pervasive scenarios based on a novel edge segment selection and conditional segment combination schemes; the method also includes a confidence measure for the detected pupil.
A neuromorphic dataset and methodology for eye tracking, harnessing event data captured streamed continuously by a Dynamic Vision Sensor, and a directly trained Spiking Neuron Network (SNN) regression model that leverages a state-of-the-art low power edge neuromorphic processor - Speck is introduced.
It is argued in this paper that investigating hardware-software co-designs would bring along opportunities to make such systems smaller and more efficient, on the wearable, embedded platform itself.
Adding a benchmark result helps the community track progress.