Interactive tracking and action retrieval to support human behavior analysis

Behavior analysis;Tracking;Action recognition;Action retrieval;Attachment security

Ciptadi, Arridhana ; Rehg, James M. Abowd, Gregory D. Computer Science Rozga, Agata Messinger, Daniel Perona, Pietro ; Rehg, James M.

University:Georgia Institute of Technology

Department:Computer Science

关键词: Behavior analysis;    Tracking;    Action recognition;    Action retrieval;    Attachment security;   

Others  :  https://smartech.gatech.edu/bitstream/1853/54987/1/CIPTADI-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

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.