{"507461":{"#nid":"507461","#data":{"type":"event","title":"PhD Dissertation Defense by Arridhana Ciptadi","body":[{"value":"\u003Cp\u003ECOMMITTEE:\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. James M. Rehg, School of Interactive Computing, Georgia Tech (Advisor) Dr. Gregory D. Abowd, School of Interactive Computing, Georgia Tech\u003C\/p\u003E\u003Cp\u003E(co-Advisor)\u003C\/p\u003E\u003Cp\u003EDr. Agata Rozga, School of Interactive Computing, Georgia Tech Dr. Daniel Messinger, Department of Psychology, University of Miami Dr. Pietro Perona, Division of Engineering and Applied Science, California Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EABSTRACT\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe goal of this thesis is to develop a set of tools for continuous tracking of behavioral phenomena in videos to support human behavior study. Current standard practices for extracting useful behavioral information from a video are typically difficult to replicate and require a lot of human time. For example, extensive training is typically required for a human coder to reliably code a particular behavior\/interaction. Also, manual coding typically takes a lot more time than the actual length of the video (e.g., it can take up to 6 times the actual length of the video to do human-assisted single object tracking). The time intensive nature of this process (due to the need to train expert and manual coding) puts a strong burden on the research process. In fact, it is not uncommon for an institution that heavily uses videos for behavioral research to have a massive backlog of unprocessed video data.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETo address this issue, I have developed an efficient behavior retrieval and interactive tracking system. These tools allow behavioral researchers\/clinicians to more easily extract relevant behavioral information, and more objectively analyze behavioral data from videos. I have demonstrated that my behavior retrieval system achieves state-of-the-art performance for retrieving stereotypical behaviors of individuals with autism in a real-world video data captured in a classroom setting. I have also demonstrated that my interactive tracking system is able to produce high-precision tracking results with less human effort compared to the state-of-the-art. I further show that by leveraging the tracking results, we can extract an objective measure based on proximity between people that is useful for analyzing certain social interactions. I validated this new measure by showing that we can use it to predict qualitative expert ratings in the Strange Situation (a procedure for studying infant attachment security), a quantity that is difficult to obtain due to the difficulty in training the human expert.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Interactive Tracking and Action Retrieval to Support Human Behavior Analysis"}],"uid":"27690","created_gmt":"2016-02-29 14:47:16","changed_gmt":"2016-10-08 02:16:45","author":"Jacquelyn Strickland","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-03-14T17:00:00-04:00","event_time_end":"2016-03-14T19:00:00-04:00","event_time_end_last":"2016-03-14T19:00:00-04:00","gmt_time_start":"2016-03-14 21:00:00","gmt_time_end":"2016-03-14 23:00:00","gmt_time_end_last":"2016-03-14 23:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"119191","name":"PhD Dissertation Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}