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###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:
  1. Canny, J., "A Computational Approach To Edge Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, 1986
  2. Viola and Jones, "Rapid object detection using a boosted cascade of simple features", Computer Vision and Pattern Recognition, 2001
  3. King Hann Lim et al. "Lane-Vehicle Detection and Tracking", IMECS, 2009

#####Training data used from: ##### California Institute of Technology SURF project

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