An automatic drowning detection surveillance system for challenging outdoor pool environments
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Rare Events in Video Using Semantic Primitives with HMM
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Universal Access in the Information Society
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Understanding human intentions via hidden markov models in autonomous mobile robots
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Using proxemics to evaluate human-robot interaction
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Proxemic feature recognition for interactive robots: automating metrics from the social sciences
ICSR'11 Proceedings of the Third international conference on Social Robotics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
International Journal of Technology Enhanced Learning
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Recent feasibility studies involving children with autism spectrum disorders (ASD) interacting with socially assistive robots have shown that some children have positive reactions to robots, while others may have negative reactions. It is unlikely that children with ASD will enjoy any robot 100% of the time. It is therefore important to develop methods for detecting negative child behaviors in order to minimize distress and facilitate effective human-robot interaction. Our past work has shown that negative reactions can be readily identified and classified by a human observer from overhead video data alone, and that an automated position tracker combined with human-determined heuristics can differentiate between the two classes of reactions. This paper describes and validates an improved, non-heuristic method for determining if a child is interacting positively or negatively with a robot, based on Gaussian mixture models (GMM) and a naive-Bayes classifier of overhead camera observations. The approach achieves a 91.4% accuracy rate in classifying robot interaction, parent interaction, avoidance, and hiding against the wall behaviors and demonstrates that these classes are sufficient for distinguishing between positive and negative reactions of the child to the robot.