###Project implements a basic realtime lane and vehicle tracking using OpenCV.###
Screenshots:
Implemented with:
- OpenCV 2.3
- C/C++ using Microsoft Visual Studio 2010 IDE.
OpenCV features used & used techniques:
- Gaussian smoothing for image noise removal
- Canny edge detection [1]
- Hough transform for line detection
- Haar features for vehicle detection (hypothesis generation) [2]
- Vehicle hypothesis verification using horizontal edges and symmetry [3]
Possible improvements:
- k-Means clustering for Hough lines
- Kalman/Gabor/RANSAC filtering of sampled data
- KLT (Kanade-Lucas-Tomasi) feature tracker for vehicle tracking
- Vanishing point detection using Gaussian probability model
- Better lane tracking(probability methods), stability & accuracy
- More accurate vehicle hypothesis checking
- Alternative IPM(inverse perspective mapping) lane detection method
- Road area extraction & detection for roads without lanes
- Ability to process night vision situations
- Include road shadow removal
- Speed upgrades
- Road sign and traffic lights detection
References:
- Canny, J., "A Computational Approach To Edge Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, 1986
- Viola and Jones, "Rapid object detection using a boosted cascade of simple features", Computer Vision and Pattern Recognition, 2001
- King Hann Lim et al. "Lane-Vehicle Detection and Tracking", IMECS, 2009
#####Training data used from: ##### California Institute of Technology SURF project
