Automated coding of activity videos from an OCD study
Joshua Fasching, Nicholas Walczak, Gail A. Bernstein, Tasoulla Hadjiyanni, Kathryn Cullen, Vassilios Morellas, Nikolaos Papanikolopoulos
Abstract
Analysis of behavior using video is a promising approach for identifying risk markers for psychopathology that can be applied in a wide range of populations. Computer vision techniques are needed to automatically code behavior in order to reduce time and effort in these analyses. This paper discusses algorithms developed for the automatic analysis of video data from a study regarding the impact of environmental factors on youths with obsessive-compulsive disorder. Overhead videos of subjects washing hands were automatically annotated for activities such as rinsing, applying soap, and turning on/off the water faucet. These automated annotations were created by using a color-based background subtraction method to create a foreground probability score which is then used to determine if various labeled regions of interest (ROIs) are activated. These activation signals are then characterized and used to determine when different substeps of the handwashing procedure are performed. Automated annotations were validated by comparisons with hand-labeled ground truth.
BibTeX
@inproceedings{icra2016_automatedcodingo,
title = {Automated coding of activity videos from an OCD study},
author = {Joshua Fasching and Nicholas Walczak and Gail A. Bernstein and Tasoulla Hadjiyanni and Kathryn Cullen and Vassilios Morellas and Nikolaos Papanikolopoulos},
booktitle = {ICRA 2016},
year = {2016}
}