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Computational Intelligence in Automotive Applications Episode 1 Part 3

Tham khảo tài liệu 'computational intelligence in automotive applications episode 1 part 3', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Visual Monitoring of Driver Inattention 25 a Frame 187 b Frame 269 c Frame 354 d Frame 454 e Frame 517 f Fig. 5. Tracking results for a sequence g To continuously monitor the driver it is important to track his pupils from frame to frame after locating the eyes in the initial frames. This can be done efficiently by using two Kalman filters one for each pupil in order to predict pupil positions in the image. We have used a pupil tracker based on 23 but we have tested it with images obtained from a car moving on a motorway. Kalman filters presented in 23 works reasonably well under frontal face orientation with open eyes. However it will fail if the pupils are not bright due to oblique face orientations eye closures or external illumination interferences. Kalman filter also fails when a sudden head movement occurs because the assumption of smooth head motion has not been fulfilled. To overcome this limitation we propose a modification consisting on an adaptive search window which size is determined automatically based on pupil position pupil velocity and location error. This way if Kalman filtering tracking fails in a frame the search window progressively increases its size. With this modification the robustness of the eye tracker is significantly improved for the eyes can be successfully found under eye closure or oblique face orientation. The state vector of the filter is represented as Xt ct rt Ut Vt where ct rt indicates the pupil pixel position its centroid and ut Vt is its velocity at time t in c and r directions respectively. Figure 5 shows an example of the pupil tracker working in a test sequence. Rectangles on the images indicate the search window of the filter while crosses indicate the locations of the detected pupils. Figure 5f g draws the estimation of the pupil positions for the sequence under test. The tracker is found to be rather robust for different users without glasses lighting conditions face orientations and distances between the camera and the