1. Video Processing EN292 Class Project By Anat Kaspi

2. The Goal Tracking vehicles by estimating the location of the vehicle’s front plane Stereo tracking - using two pairs of camera Assumptions The front part of the car is planner Car is moving in straight line - only translating in the x direction The video is taking by two synchronized cameras

3. The Algorithm Refine the location by searching for the best location in square error meaning Determine the starting location point for frame n+1 Estimating the vehicle motion from frame n to frame n+1 using Kalman Filter Starting location for the vehicle at frame n

4. Set up Collecting videos with two cameras – stereo Mark the road for reference points Calibration for the two cameras Calibration Using calibration tool in VXL - …\brl\bmvl\bmvv\mvbin\cal Using know points on the road

5. Estimating the plane location Assumptions Projected world point of Lambertian surface into two images will have the same intensity in both images Projected world point of Lambertian surface into two images will have the same intensity in both images Create Synthetic images from the stereo pair in order to have Lambertian surface Create Synthetic images from the stereo pair in order to have Lambertian surfaceProcess Edge detection on the images Edge detection on the images more stable more stable Create binary image from the edge map Create binary image from the edge map Smooth the image – relative distance Smooth the image – relative distance

6. Estimating the plane location Looking to minimize the overall error Sample points on the plane (x,y,z,1) – world point P_L, P_R – projection matrix

7. Motion Estimation Using Kalman filter - prediction and correction loop The General model State dynamics X(n+1) = A(n)*X(n)+W(n) Observation model Y(n) = X(n)+V(n) Update equation Weighted average of the present value and the present observation

8. Motion Estimation X(n) – distance the vehicle is traveling between two frames Only translating – x(n) scalar The motion model X(n+1) = X(n)+W(n) W(n)~N(0,sigma_w) white noise The observation model Y(n) = X(n)+V(n) V(n)~N(0,sigma_v) white noise Update equation

9. Results… Running software in class…

10. Thank you !