eebear1221 / lane_detection-3

LD using Computer Vision

Lane_Detection

The aim of this project is to develop and implement a Computer Vision based algorithm such that:
1. All visible lane boundaries are detected for urban/highway roads under close to idle weather conditions

Dependencies:
1. skimage
2. OpenCV >= 2.4.6
3. numpy

Code Files:
1. GenerateBEV.py - Code from KITTI Road Dataset to obtain IPM view of an Image using a calibrated camera.
2. Lane_Detection.py - Detects multiple Lanes taking the IPM image as input.
3. Inverse_Perspective_Mapping.py - Generic code for obtaining the BEV of an image. Calculates the homography matrix by using the extrinc and intrinsic camera parameters.

To Do:
For detecting Curved Lanes - Curve fitting using Ransac

Process Pipeline:

Original Image	Input IPM Image	Filtered IPM Image	Thresholded Image	Binary Image After Selecting ROI	Initial Guess for Ransac	Lane Detected Image After Ransac and Eliminating False Lanes

Lane Detection for Test Images:

Orginal Image	Input IPM Image (KITTI)	IPM Image After Lane Detection

Original Image (ROMA)	Input Approx IPM Image (Roma)	IPM Image After Lane Detection