PhD Defense by Arridhana Ciptadi

Event Details
  • Date/Time:
    • Monday March 14, 2016 - Tuesday March 15, 2016
      4:00 pm - 5:59 pm
  • Location: TSRB 223
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Summary Sentence: Interactive Tracking and Action Retrieval to Support Human Behavior Analysis

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Ph.D. Defense of Dissertation Announcement


Title: Interactive Tracking and Action Retrieval to Support Human Behavior Analysis


Arridhana Ciptadi

Ph.D. Candidate

School of Interactive Computing

College of Computing

Georgia Institute of Technology


Date: Monday, March 14th, 2016

Time: 4PM to 6PM EST

Location: TSRB 223




Dr. James M. Rehg, School of Interactive Computing, Georgia Tech (Advisor) Dr. Gregory D. Abowd, School of Interactive Computing, Georgia Tech


Dr. 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




The 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.


To 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.

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In Campus Calendar

Graduate Studies

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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Mar 14, 2016 - 5:24am
  • Last Updated: Oct 7, 2016 - 10:16pm