{"title":"从感兴趣的动态区域自动过滤眼睛注视指标","authors":"Gavindya Jayawardena, S. Jayarathna","doi":"10.1109/IRI49571.2020.00018","DOIUrl":null,"url":null,"abstract":"Eye-tracking experiments usually involves areas of interests (AOIs) for the analysis of eye gaze data as they could reveal potential cognitive load, and attentional patterns yielding interesting results about participants. While there are tools to define AOIs to extract eye movement data for the analysis of gaze measurements, they may require users to draw boundaries of AOIs on eye tracking stimuli manually or use markers to define AOIs in the space to generate AOI-mapped gaze locations. In this paper, we introduce a novel method to dynamically filter eye movement data from AOIs for the analysis of advanced eye gaze metrics. We incorporate pre-trained object detectors for offline detection of dynamic AOIs in dynamic eye-tracking stimuli such as video streams. We present our implementation and evaluation of object detectors to find the best object detector to be integrated in a real-time eye movement analysis pipeline to filter eye movement data that falls within the polygonal boundaries of detected dynamic AOIs. Our results indicate the utility of our method by applying it to a publicly available dataset.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"107 1","pages":"67-74"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automated Filtering of Eye Gaze Metrics from Dynamic Areas of Interest\",\"authors\":\"Gavindya Jayawardena, S. Jayarathna\",\"doi\":\"10.1109/IRI49571.2020.00018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Eye-tracking experiments usually involves areas of interests (AOIs) for the analysis of eye gaze data as they could reveal potential cognitive load, and attentional patterns yielding interesting results about participants. While there are tools to define AOIs to extract eye movement data for the analysis of gaze measurements, they may require users to draw boundaries of AOIs on eye tracking stimuli manually or use markers to define AOIs in the space to generate AOI-mapped gaze locations. In this paper, we introduce a novel method to dynamically filter eye movement data from AOIs for the analysis of advanced eye gaze metrics. We incorporate pre-trained object detectors for offline detection of dynamic AOIs in dynamic eye-tracking stimuli such as video streams. We present our implementation and evaluation of object detectors to find the best object detector to be integrated in a real-time eye movement analysis pipeline to filter eye movement data that falls within the polygonal boundaries of detected dynamic AOIs. Our results indicate the utility of our method by applying it to a publicly available dataset.\",\"PeriodicalId\":93159,\"journal\":{\"name\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"volume\":\"107 1\",\"pages\":\"67-74\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI49571.2020.00018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Filtering of Eye Gaze Metrics from Dynamic Areas of Interest
Eye-tracking experiments usually involves areas of interests (AOIs) for the analysis of eye gaze data as they could reveal potential cognitive load, and attentional patterns yielding interesting results about participants. While there are tools to define AOIs to extract eye movement data for the analysis of gaze measurements, they may require users to draw boundaries of AOIs on eye tracking stimuli manually or use markers to define AOIs in the space to generate AOI-mapped gaze locations. In this paper, we introduce a novel method to dynamically filter eye movement data from AOIs for the analysis of advanced eye gaze metrics. We incorporate pre-trained object detectors for offline detection of dynamic AOIs in dynamic eye-tracking stimuli such as video streams. We present our implementation and evaluation of object detectors to find the best object detector to be integrated in a real-time eye movement analysis pipeline to filter eye movement data that falls within the polygonal boundaries of detected dynamic AOIs. Our results indicate the utility of our method by applying it to a publicly available dataset.