{"630447":{"#nid":"630447","#data":{"type":"event","title":"PhD Defense by Eunji Chong","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Computational Methods for Measurement of Visual Attention from Videos towards Large-scale Behavioral Analysis\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EEunji Chong\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of\u0026nbsp;Computer\u0026nbsp;Science\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECollege of Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E:\u0026nbsp; Thursday, January 9th, 2020\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETime:\u0026nbsp;3:30 - 5:30 PM (EST)\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELocation:\u0026nbsp;TSRB 222\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. James M. Rehg (Advisor), School of\u0026nbsp;Computer\u0026nbsp;Science, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Agata Rozga, School of\u0026nbsp;Computer\u0026nbsp;Science, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Gregory D. Abowd, School of\u0026nbsp;Computer\u0026nbsp;Science, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Irfan Essa, School of\u0026nbsp;Computer\u0026nbsp;Science, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yaser Sheikh, Robotics Institute, Carnegie Mellon University\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EVisual attention is a critically-important aspect of human social behavior, visual navigation, and interaction with the 3D environment, and where and what people are paying attention to reveals a lot of information about their social, cognitive, and affective states. While monitor-based and wearable eye trackers are widely available, they are not sufficient to support the large-scale collection of naturalistic gaze data in contexts such as face-to-face social interactions or object manipulation in 3D environments. Wearable eye trackers are burdensome to participants and bring issues of calibration, compliance, cost, and battery life.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis thesis investigates different ways to measure real-world human visual attention using computer vision from plain videos and its use for identifying meaningful social behaviors. Specifically, three methods are investigated. First, I present methods for detection of looks to camera in first-person view and its use for eye contact detection. Experimental results show that the presented method can achieve the first human expert-level detection of eye contact. Second, I develop a method for tracking heads in a 3d space for measuring attentional shifts. Lastly, I propose spatiotemporal deep neural networks for detecting time-varying attention targets in video and present its application for the detection of shared attention and joint attention. The final method achieves state-of-the-art results on different benchmark datasets on attention measurement as well as the first empirical result on clinically-relevant gaze shift classification.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPresented approaches have the benefit of linking gaze estimation to the broader tasks of action recognition and dynamic visual scene understanding, and bears potential as a useful tool for understanding attention in various contexts such as human social interactions, skill assessments, and human-robot interactions.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Computational Methods for Measurement of Visual Attention from Videos towards Large-scale Behavioral Analysis"}],"uid":"27707","created_gmt":"2020-01-02 19:02:41","changed_gmt":"2020-01-02 19:02:41","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-01-09T15:30:00-05:00","event_time_end":"2020-01-09T17:30:00-05:00","event_time_end_last":"2020-01-09T17:30:00-05:00","gmt_time_start":"2020-01-09 20:30:00","gmt_time_end":"2020-01-09 22:30:00","gmt_time_end_last":"2020-01-09 22:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}