Content-Aware Eye Tracking for Autostereoscopic 3D Display
This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4787 |
| _version_ | 1827707750521503744 |
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| author | Dongwoo Kang Jingu Heo |
| author_facet | Dongwoo Kang Jingu Heo |
| author_sort | Dongwoo Kang |
| collection | DOAJ |
| description | This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions. |
| first_indexed | 2024-03-10T16:53:04Z |
| format | Article |
| id | doaj.art-901bf0cbe01447189bb6db3723e20236 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:53:04Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-901bf0cbe01447189bb6db3723e202362023-11-20T11:15:20ZengMDPI AGSensors1424-82202020-08-012017478710.3390/s20174787Content-Aware Eye Tracking for Autostereoscopic 3D DisplayDongwoo Kang0Jingu Heo1Multimedia Processing Lab, Samsung Advanced Institute of Technology, Suwon 16678, KoreaMultimedia Processing Lab, Samsung Advanced Institute of Technology, Suwon 16678, KoreaThis study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.https://www.mdpi.com/1424-8220/20/17/4787eye detectioneye trackingcontent-aware eye alignmenterror reinforcement learningautostereoscopic three-dimensional displayaugmented reality display |
| spellingShingle | Dongwoo Kang Jingu Heo Content-Aware Eye Tracking for Autostereoscopic 3D Display Sensors eye detection eye tracking content-aware eye alignment error reinforcement learning autostereoscopic three-dimensional display augmented reality display |
| title | Content-Aware Eye Tracking for Autostereoscopic 3D Display |
| title_full | Content-Aware Eye Tracking for Autostereoscopic 3D Display |
| title_fullStr | Content-Aware Eye Tracking for Autostereoscopic 3D Display |
| title_full_unstemmed | Content-Aware Eye Tracking for Autostereoscopic 3D Display |
| title_short | Content-Aware Eye Tracking for Autostereoscopic 3D Display |
| title_sort | content aware eye tracking for autostereoscopic 3d display |
| topic | eye detection eye tracking content-aware eye alignment error reinforcement learning autostereoscopic three-dimensional display augmented reality display |
| url | https://www.mdpi.com/1424-8220/20/17/4787 |
| work_keys_str_mv | AT dongwookang contentawareeyetrackingforautostereoscopic3ddisplay AT jinguheo contentawareeyetrackingforautostereoscopic3ddisplay |