Multimodal human-computer interaction: A survey
Computer Vision and Image Understanding
Hands-free vision-based interface for computer accessibility
Journal of Network and Computer Applications
Multimodal interfaces: Challenges and perspectives
Journal of Ambient Intelligence and Smart Environments
ERCIM'06 Proceedings of the 9th conference on User interfaces for all
Blink and wink detection for mouse pointer control
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Towards hands-free interfaces based on real-time robust facial gesture recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Multi-dimensional game interface with stereo vision
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
Multimodal human computer interaction: a survey
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
FaceMouse: a human-computer interface for tetraplegic people
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Multimodal interfaces: Challenges and perspectives
Journal of Ambient Intelligence and Smart Environments
Proceedings of the 13th International Conference on Interacción Persona-Ordenador
Hi-index | 0.00 |
Tracking methods are evaluated in a real-time feature tracking system used for human-computer interaction (HCI).The Camera Mouse, a HCI system for people with severe disabilities that interprets video input to manipulate the mouse pointer [1], was improved and used as the test platform for this study.Tracking methods tested are the Lucas-Kanade tracker [6] and a tracker based on normalized correlation [1].Both methods are evaluated with and without multidimensional Kalman filters.Two-, four-, and six-dimensional filters are tested to model feature location, velocity, and acceleration.The various tracker and filter combinations are evaluated for accuracy, computational efficiency, and practiality.The normalized correlation coefficient tracker without Kalman filtering is found to be the tracker best suited for a variety of HCI tasks.