Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Footing in human-robot conversations: how robots might shape participant roles using gaze cues
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Recognizing engagement in human-robot interaction
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
A survey on vision-based human action recognition
Image and Vision Computing
2010 Special Issue: Detecting contingencies: An infomax approach
Neural Networks
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Vision-based contingency detection
Proceedings of the 6th international conference on Human-robot interaction
Human behavior understanding for inducing behavioral change: application perspectives
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
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Critical to natural human-robot interaction is the capability of robots to detect the contingent reactions by humans. In various interaction scenarios, a robot can recognize a human's intention by detecting the presence or absence of a human response to its interactive signal. In our prior work [1], we addressed the problem of detecting visible reactions by developing a method of detecting changes in human behavior resulting from a robot signal. We extend the previous behavior change detector by integrating multiple cues using a mechanism that operates at two levels of information integration and then adaptively applying these cues based on their reliability. We propose a new method for evaluating reliability of cues online during interaction. We perform a data collection experiment with help of the Wizard-of-Oz methodology in a turn-taking scenario in which a humanoid robot plays the turn-taking imitation game “Simon says” with human partners. Using this dataset, which includes motion and body pose cues from a depth and color image, we evaluate our contingency detection module with the proposed integration mechanisms and show the importance of selecting the appropriate level of cue integration.