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Video Processing EN292

Video Processing EN292. Class Project By Anat Kaspi. 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

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Video Processing EN292

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

  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 • Create Synthetic images from the stereo pair in order to have Lambertian surface Process • Edge detection on the images more stable • Create binary image from the edge map • Smooth the image – relative distance

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

  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 !

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