Computer Vision for Interactive Computer Graphics
IEEE Computer Graphics and Applications
Finger Tracking for the Digital Desk
AUIC '00 Proceedings of the First Australasian User Interface Conference
Bare-hand human-computer interaction
Proceedings of the 2001 workshop on Perceptive user interfaces
Improved Face and Hand Tracking for Sign Language Recognition
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Vision-based human-computer interface using hand gestures
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Location Prediction for Tracking Moving Objects Based on Grey Theory
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Visual Mouse: SIFT Detection and PCA Recognition
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Face and Hands Detection and Tracking Applied to the Monitoring of Medication Intake
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Robust hand tracking using a simple color classification technique
VRCAI '08 Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01
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In the field of human computer interaction (HCI), hand has been widely used as input device for natural interaction. However, during hand tracking, the continuously changing of hand shape and the interference from distractors (faces or hands) or occlusion reduce the robustness of man-machine alternation. In this paper, we use a web camera to detect two hands automatically and then track them stably in order to go against the problems mentioned above. At the first stage, we put forward a contour-based method to extract five fingertips which provide cues to locate initial hand position. At the second stage, CamShift is adopted to track the located hands. However, the means may easily lose the tracked objects due to its inadaptability to distractors and occlusion. Hence, we employ an improved Grey Model which is a good predictor of historical data to guide CamShift so as to achieve more accurate tracking. Experiments have been divided into two groups including the distractors tests and the occlusion tests. The convincing results illustrate the effectiveness of the proposed algorithm.