19 June 2017 Real time eye tracking using Kalman extended spatio-temporal context learning
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Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 104431G (2017) https://doi.org/10.1117/12.2280271
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
Abstract
Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.
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Farzeen Munir, Fayyaz ul Amir Asfar Minhas, Abdul Jalil, Moongu Jeon, "Real time eye tracking using Kalman extended spatio-temporal context learning", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431G (19 June 2017); doi: 10.1117/12.2280271; https://doi.org/10.1117/12.2280271
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