Advanced_Lane_Lines_Project4

In this project, computer vision techniques are used to accurately detect lanes in a video. The video processing pipeline process_vid(), consists of the following steps:

1. Receiving input frame as an RGB image
2. Undistorting the image, using the calibration matrix mtx
3. Applying Multiple threshholds to the undistorted image, including:
   • Gradient: Sobel in both X and Y directions
   • Color: Using L component of LUV colorspace, and B component of LAB colorspace
4. Warping the image using the Transform Matrix M into a bird's eye view
5. Running detect_lanes() and look_ahead() functions to detect the lane lines
6. Smoothing the lanes detected along with the last n lane detects
7. Unwarping the detected lanes onto the image and returning the original image along with:
   • The Lane Covered in Green
   • Left Lane highlighted in Blue
   • Right Lane highlighted in Red
   • Lane's Radius of Curvature in meters
   • Car's absolute offset from the center of the lane in meters

Check this video to preview the pipeline's output

Here is a youtube link for my video to stream it online.

Please check the writeup report for further details *Also check my implementation contained in this IPython Notebook