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.
|Original language||English (US)|
|Title of host publication||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jun 8 2016|
|Event||2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden|
Duration: May 16 2016 → May 21 2016
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Other||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Period||5/16/16 → 5/21/16|
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© 2016 IEEE.
Copyright 2016 Elsevier B.V., All rights reserved.