A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
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
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Autonomous Mental Development
Adaptive integration of multiple cues for contingency detection
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
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We present a novel method for the visual detection of a contingent response by a human to the stimulus of a robot action. Contingency is defined as a change in an agent's behavior within a specific time window in direct response to a signal from another agent; detection of such responses is essential to assess the willingness and interest of a human in interacting with the robot. Using motion-based features to describe the possible contingent action, our approach assesses the visual self-similarity of video subsequences captured before the robot exhibits its signaling behavior and statistically models the typical graph-partitioning cost of separating an arbitrary subsequence of frames from the others. After the behavioral signal, the video is similarly analyzed and the cost of separating the after-signal frames from the before-signal sequences is computed; a lower than typical cost indicates likely contingent reaction. We present a preliminary study in which data were captured and analyzed for algorithmic performance.