2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011 · 22 citations · 0 references
EngineeringComputer ScienceRobotics EngineeringComputer VisionHumanoid RoboticsRoboticsAction RecognitionMachine Vision For RoboticsMotion AnalysisPerception For Humanoid RobotsPose EstimationRobotic VisionActivity RecognitionAsynchronous Sensor FusionComputer Vision For RoboticsDomain Knowledge IntegrationIntention RecognitionMotion DetectionMovement RecognitionRobot Intention Recognition Cues
In this paper, a multi-level approach to intention, activity, and motion recognition for a humanoid robot is proposed. Our system processes images from a monocular camera and combines this information with domain knowledge. The recognition works on-line and in real-time, it is independent of the test person, but limited to predefined view-points. Main contributions of this paper are the extensible, multi-level modeling of the robot's vision system, the efficient activity and motion recognition, and the asynchronous information fusion based on generic processing of mid-level recognition results. The complementarity of the activity and motion recognition renders the approach robust against misclassifications. Experimental results on a real-world data set of complex kitchen tasks, e.g., Prepare Cereals or Lay Table, prove the performance and robustness of the multi-level recognition approach.