Tracking lanes

I have taken the caltech cordova1 dataset (search of the term will find the location to download) to test the algorithm I develop to detect lanes. Here is the algorithm in short (without kalman filter and noise elimination):

First convert to bird eye view of the frame
Then convert the image to grayscale
Apply Gaussian filter to smooth out outliers
Use canny method to create binary image (detect edges)
Run probabilistic hough transform to get the lines
Remove lines with wrong angle and size
Use perspective transform to get image co-ordinates
Draw the lines on original frame
Draw a green line to show the vehicle path and yellow circle to show the vanishing point.

Next step would be to kalman filter to deal with noises present in the detection using vehicle speed/ acceleration (and may be other sensors) to establish prior belief. Then will use the current detected lanes and update the belief. I'll be using estimated vehicle speed and use dataset 1 for training and then test using other datasets to measure its performance.  Once it works I'll be using RANSAC to avoid going through all the points - its kind of slow otherwise (if Jetson TX1 performs around 20 fps I probably wont bother).

1 comment:

  1. The two Direct CNC unique corporations, 3D Systems and Stratasys, solidified their hold available on the market by buying opponents like Phenix, Makerbot, DTM, Objet, and extra.The industry was removed from a monopoly, nonetheless. Huge numbers of new opponents offered affordable machines that rivaled industrial 3D printers. Examples include Ultimaker, Lulzbot, and Prusa 3D printers in the desktop and DIY 3D printer kit markets, and Desktop Metal, Markforged, and Carbon 3D in the industrial sector.


Student registration reached 365 in less than a week

As I have posted previously, I asked students to register for Autonomous Vehicle course and within less than a week we have 365 registration...