TY - JOUR
T1 - Automated coding of activity videos from an OCD study
AU - Fasching, Joshua
AU - Walczak, Nicholas
AU - Bernstein, Gail A.
AU - Hadjiyanni, Tasoulla
AU - Cullen, Kathryn
AU - Morellas, Vassilios
AU - Papanikolopoulos, Nikolaos
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84977581462&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977581462&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2016.7487783
DO - 10.1109/ICRA.2016.7487783
M3 - Article
AN - SCOPUS:84977581462
SN - 1050-4729
SP - 5638
EP - 5643
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Y2 - 16 May 2016 through 21 May 2016
ER -