Date post: | 19-Dec-2015 |
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The GoalThe GoalTracking vehicles by estimating the location of the Tracking vehicles by estimating the location of the vehicle’s front planevehicle’s front plane
Stereo tracking - using two pairs of cameraStereo tracking - using two pairs of camera
AssumptionsAssumptions
The front part of the car is plannerThe front part of the car is planner
Car is moving in straight line - only translating in the x Car is moving in straight line - only translating in the x directiondirection
The video is taking by two synchronized camerasThe video is taking by two synchronized cameras
The AlgorithmThe 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
Set upSet upCollecting videos with two cameras – stereoCollecting videos with two cameras – stereo
Mark the road for reference pointsMark the road for reference points
Calibration for the two camerasCalibration for the two cameras
CalibrationCalibration
Using calibration tool in VXL - …\brl\bmvl\bmvv\Using calibration tool in VXL - …\brl\bmvl\bmvv\mvbin\calmvbin\cal
Using know points on the roadUsing know points on the road
Estimating the plane locationEstimating the plane locationAssumptionsAssumptions
Projected world point of Lambertian surface into two images will Projected world point of Lambertian surface into two images will have the same intensity in both imageshave the same intensity in both images
Create Synthetic images from the stereo pair in order to have Create Synthetic images from the stereo pair in order to have Lambertian surface Lambertian surface
ProcessProcess Edge detection on the images Edge detection on the images
more stable more stable Create binary image from the edge mapCreate binary image from the edge map Smooth the image – relative distanceSmooth the image – relative distance
Estimating the plane locationEstimating the plane location
Looking to minimize the overall errorLooking to minimize the overall error
Sample points on the planeSample points on the plane
(x,y,z,1) – world point(x,y,z,1) – world pointP_L, P_R – projection matrixP_L, P_R – projection matrix
2)1,,,()1,,,( zyxPIzyxPIError rightleft
Motion Estimation Motion Estimation
Using Kalman filterUsing Kalman filter - prediction and correction loop - prediction and correction loopThe General modelThe General modelState dynamicsState dynamics
X(n+1) = A(n)*X(n)+W(n)X(n+1) = A(n)*X(n)+W(n)
Observation modelObservation model
Y(n) = X(n)+V(n)Y(n) = X(n)+V(n)Update equationUpdate equation
Weighted average of the present value and the present observationWeighted average of the present value and the present observation
)()()(ˆ)(1)()1(ˆ nynGnxnGnAnx
1222 )()()()(
nnnnG v
)()()(1)()()1( 222 nnAnGnnAn wT
Motion EstimationMotion EstimationX(n) – distance the vehicle is traveling between two framesX(n) – distance the vehicle is traveling between two frames
Only translating – x(n) scalarOnly translating – x(n) scalar
The motion modelThe motion model
X(n+1) = X(n)+W(n) X(n+1) = X(n)+W(n)
W(n)~N(0,sigma_w) white noiseW(n)~N(0,sigma_w) white noise
The observation modelThe observation model
Y(n) = X(n)+V(n) Y(n) = X(n)+V(n)
V(n)~N(0,sigma_v) white noiseV(n)~N(0,sigma_v) white noise
Update equationUpdate equation
)()()(ˆ)(1)1(ˆ nynGnxnGnx
)()(
)()( 22
2
nn
nnG
v
)()(1)()1( 222 nnGnn w
211 1
11
nnn ynn
n