f32b68852852224fa4525476108da10b pmc_34300511.pdf 62dcb77f3e325cae0fd1b35238bd933193f2ef09 pmc_34300511.pdf d6a9ffd30737eb46c3e16922f332e07c43eb70d9fbb64affd5333295471c77a2 pmc_34300511.pdf Title: OpenEDS2020 Challenge on Gaze Tracking for VR:Dataset and Results Subject: This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the future based on a sequence of previous eye images, and (2) Sparse Temporal Semantic Segmentation Challenge, with the goal of using temporal information to propagate semantic eye labels to contiguous eye image frames. Both competitions were based on the OpenEDS2020 dataset, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display with two synchronized eye-facing cameras. The dataset, which we make publicly available for the research community, consists of 87 subjects performing several gaze-elicited tasks, and is divided into 2 subsets, one for each competition task. The proposed baselines, based on deep learning approaches, obtained an average angular error of 5.37 degrees for gaze prediction, and a mean intersection over union score (mIoU) of 84.1% for semantic segmentation. The winning solutions were able to outperform the baselines, obtaining up to 3.17 degrees for the former task and 95.2% mIoU for the latter. Keywords: gaze prediction; semantic segmentation; gaze estimation; video oculography; virtual reality Author: Cristina Palmero, Abhishek Sharma, Karsten Behrendt, Kapil Krishnakumar, Oleg V. Komogortsev, Sachin S. Talathi Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Jul 13 10:40:33 2021 CEST ModDate: Tue Jul 13 11:26:33 2021 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 8910468 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- TEGMIU+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 VTAGDO+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 RMSSCI+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 49 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 53 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 57 0 CNIGIN+CMSY10 Type 1 Builtin yes yes no 69 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 87 0 URFODN+CMR10 Type 1 Builtin yes yes no 91 0 VUVJZQ+URWPalladioL-BoldItal Type 1 Custom yes yes no 146 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-02 03:10:05 CEST RepresentationInformation: pmc_34300511.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-03-31 12:14:36 CEST Size: 8910468 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 322 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: OpenEDS 2020 Challenge: Description Destination: section.2 Children: Item: Title: Gaze Prediction Challenge Destination: subsection.2.1 Item: Title: Sparse Temporal Semantic Segmentation Challenge Destination: subsection.2.2 Item: Title: OpenEDS2020 Dataset Destination: section.3 Children: Item: Title: Data Collection Destination: subsection.3.1 Item: Title: Gaze Prediction Destination: subsection.3.2 Children: Item: Title: Dataset Curation Destination: subsubsection.3.2.1 Item: Title: Annotations Destination: subsubsection.3.2.2 Item: Title: Semantic Eye Segmentation Destination: subsection.3.3 Children: Item: Title: Dataset Curation Destination: subsubsection.3.3.1 Item: Title: Annotations Destination: subsubsection.3.3.2 Item: Title: Baseline Methodologies Destination: section.4 Children: Item: Title: Gaze Prediction Destination: subsection.4.1 Item: Title: Semantic Eye Segmentation Destination: subsection.4.2 Item: Title: Challenge Participation and Winning Solutions Destination: section.5 Children: Item: Title: Gaze Prediction Challenge Winners Destination: subsection.5.1 Item: Title: Sparse Semantic Segmentation Challenge Winners Destination: subsection.5.2 Item: Title: Conclusions Destination: section.6 Item: Title: References Destination: section.6 Info: Title: OpenEDS2020 Challenge on Gaze Tracking for VR:Dataset and Results Author: Cristina Palmero, Abhishek Sharma, Karsten Behrendt, Kapil Krishnakumar, Oleg V. Komogortsev, Sachin S. Talathi Subject: This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the future based on a sequence of previous eye images, and (2) Sparse Temporal Semantic Segmentation Challenge, with the goal of using temporal information to propagate semantic eye labels to contiguous eye image frames. Both competitions were based on the OpenEDS2020 dataset, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display with two synchronized eye-facing cameras. The dataset, which we make publicly available for the research community, consists of 87 subjects performing several gaze-elicited tasks, and is divided into 2 subsets, one for each competition task. The proposed baselines, based on deep learning approaches, obtained an average angular error of 5.37 degrees for gaze prediction, and a mean intersection over union score (mIoU) of 84.1% for semantic segmentation. The winning solutions were able to outperform the baselines, obtaining up to 3.17 degrees for the former task and 95.2% mIoU for the latter. 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