Identifying the addressee in human-human-robot interactions based on head pose and speech
Proceedings of the 6th international conference on Multimodal interfaces
Dialog in the open world: platform and applications
Proceedings of the 2009 international conference on Multimodal interfaces
Models for multiparty engagement in open-world dialog
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Receptionist or information kiosk: how do people talk with a robot?
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Human Upper Body Pose Recognition Using Adaboost Template for Natural Human Robot Interaction
CRV '10 Proceedings of the 2010 Canadian Conference on Computer and Robot Vision
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
How can i help you': comparing engagement classification strategies for a robot bartender
Proceedings of the 15th ACM on International conference on multimodal interaction
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For robots to perform interaction with multiple persons, they have to be able to identify the addressees to interact with. We classify the methods of addressee detection and selection into two categories, namely, passive and active approaches. For passive approaches, the robot is programmed to detect a predefined signal, e.g., a voice command or a specific gesture, from a person who is supposed to be the addressee. In contrast, for active approaches, the robot is able to select a person as an addressee based on subtle cues that are inferred from the human pose, gaze, and facial expression. We present two new approaches for attention-based addressee selection, one is a passive method and the other is an active method. The passive method is designed for the robot to recognize common hand-waving gesture, where a Bayessian ensemble approach is proposed to fuse hand detections from depth segmentation, palm shape, skin color, and body pose. The active method is developed for the robot to perform natural interaction with multiple persons. It employs a novel human attention estimation algorithm based on human detection, tracking, upper body pose recogni-tion, face detection, gaze detection, lip motion analysis, and facial expression recognition. Extensive experiments have been conducted and the effectiveness of the proposed approaches is reported.