Pupil Detection for Augmented and Virtual Reality based on Images with Reduced Bit Depths

Gernot Fiala, Zhenyu Ye, Christian Steger

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


For future augmented reality (AR) and virtual reality (VR) applications, several different kinds of sensors will be used. These sensors, to give some examples, are used for gesture recognition, head pose tracking and pupil tracking. All these sensors send data to a host platform, where the data must be processed in real-time. This requires high processing power which leads to higher energy consumption. To lower the energy consumption, optimizations of the image processing system are necessary. This paper investigates pupil detection for AR/VR applications based on images with reduced bit depths. It shows that images with reduced bit depths even down to 3 or 2 bits can be used for pupil detection, with almost the same average detection rate. Reduced bit depths of an image reduces the memory foot-print, which allows to perform in-sensor processing for future image sensors and provides the foundation for future in-sensor processing architectures.
Original languageEnglish
Title of host publication2022 IEEE Sensors Applications Symposium (SAS 2022)
PublisherIEEE Xplore
Number of pages5
ISBN (Electronic)978-1-6654-0981-0
ISBN (Print)978-1-6654-0982-7
Publication statusPublished - 12 Sept 2022
Event17th IEEE Sensors Applications Symposium: SAS 2022 - Hybrider Event, Sundsvall, Sweden
Duration: 1 Aug 20223 Aug 2022


Conference17th IEEE Sensors Applications Symposium
Abbreviated titleSAS 2022
CityHybrider Event, Sundsvall

ASJC Scopus subject areas

  • Computer Science(all)


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