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Ph.D. Thesis Proposal Announcement
Title: Interactive Tracking and Action Retrieval to Support Human Behavior Analysis
Arridhana Ciptadi
Ph.D. Student
School of Interactive Computing
College of Computing
Georgia Institute of Technology
Date: Tuesday, June 16th, 2015
Time: 2PM to 4PM EST
Location: Klaus 1212
Committee:
Dr. James M. Rehg, School of Interactive Computing, Georgia Tech (co-Advisor)
Dr. Gregory D. Abowd, School of Interactive Computing, Georgia Tech (co-Advisor)
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
Abstract:
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. The time intensive nature of
this process puts a strong limitation on the scalability of the study.
Furthermore, this makes it difficult for a third party to perform
replication study.
To address this issue, I have developed an efficient interactive
tracking and behavior retrieval 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 achieve
state-of-the-art performance in a preliminary experiment. In the
proposed work, I will perform a more thorough evaluation of this system
on a wider set of behaviors. 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. In the
proposed work, I will show how a proximity measure that is derived from
the results of interactive tracking can be used to predict attachment
classification in The Strange Situation, a protocol for studying infant
attachment security. In addition, I will also show how this measure
opens a new avenue of behavioral research by showing that it can be used
to quantify the consistency of infant behavior in the two reunion
episodes in Strange Situation.
