Research On The Technology For Driver's Eye Tracking Based On Sampling Strong Tracking Nonlinear Filter Theory

The research of driver eye movement mechanism and nonlinear eye tracking is one of hotspots in the assistant safe driving system based on the relationship between driver eye tracking and driving status. This paper achievements on tacking the conflicts about accuracy, robustness and real time of driver nonlinear eye tracking are classified into the following categories.The first is to propose a new driver eye detection based on 2-D Log-Gabor filter. Research on the algorithm of Harr feature method for eye detection, the accuracy of eye detection is low under night driving condition. We proposed the 2-D Log-Gabor filter to eye detection in night driving condition, which avoids the effect of illumination for image.The second is to propose a novel adaptive fuzzy strong tracking finite-difference extended kalman filter for driver eye tracking. The basic work is a study on the eye movement mechanism of the realistic driving conditions based on the eye anatomical vision physiology. By monitoring the residual mean and standard deviation, the fuzzy logic adaptive controller of this method dynamically adjusts the softening factor according to fuzzy rules. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The experimental results show the validity of our method for eye tracking under realistic conditions.The third category is low complicated adaptive strong tracking simplified unscented Kalman filter(STS-UKF) for driver eye tracking. The reduced sigma points UT parameterizations can capture distribution information comparable to that of the 2n +1 symmetric UT, and reduce computational resources comparable to the n+2 UT. At the same time, strong tracking filtering (STF) is introduced into simplified UKF to improve the robustness. Suboptimal fading factors of STF are used to time update equations and measurement update equations to improve robustness of algorithm. The theory analysis and simulations show the STS-UKF can improve the computational efficiency and robustness for real-time eye tracking.The fourth is that we proposed a novel eye tracking base on multiresolution decomposition strong tracking particle filter. Nonintrusive methods for eye tracking are important driver fatigue detection. In order to improve the accuracy and stability of eye tracking, we can use the UKF to generate the proposal distribution for the PF (Particle Filter). Then, we can reduce the variance of important weights of above particle filter using wavelet multiresolution decomposition because of the wavelet multiresolution decomposition having a good property of denoising, which can improve the accuracy and robustness of eye tracking under realistic driving condition. At same time,we introduce STF into partice filter to resolve the nonlinear tracking of eye movement. The experimental results and the theoretical analysis show that the proposed method achieves higher estimation accuracy and robustness of eye tracking to head rotation, light variations and non-linear estimation in realistic driving condition.The last is to propose a scheme of drive fatigue detection based eye tracking. After studing the driver fatigue detection technology, we discuss the related driver fatigue detection based eye tracking. proposed a driver fatigue detection system model under realistic driving condition.In summary, the thesis mainly focuses on the research of technology for driver's eye tracking based on sampling strong tracking nonlinear filter theory. The quantitative evaluation of eye tracking accuracy, robustness and real time performance will be helpful to design the algorithm for driver fatigue detection system.