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Self-Driving Car Engineer Nanodegree

OpenCV and State Vector Machine

Project: track vehicles on road and place bounding box

Video of the trained solution

Overview

This project is a software pipeline to detect vehicles in a video. A detailed writeup of the solution as well as sample images of different stages of the pipeline is available in writeup_submission.md

The Project

The goals / steps of this project are the following:

  • Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
  • Optionally, apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
  • Note: for first two steps features are normalized and a selection for training and testing is randomized.
  • Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
  • Run pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
  • Estimate a bounding box for vehicles detected.

Here are links to the labeled data for vehicle and non-vehicle examples to train your classifier. These example images come from a combination of the GTI vehicle image database, the KITTI vision benchmark suite, and examples extracted from the project video itself. You are welcome and encouraged to take advantage of the recently released Udacity labeled dataset to augment your training data.

Some example images for testing your pipeline on single frames are located in the test_images folder.

Files

  • utils.py - utility and helper functions as well as feature extractors
  • buildCLF.py - builds and trains a linear SVM classifier
  • trackerDriver.py - main application entry and driver
  • vehicleTracker.ipynb - python notebook used for visualization and testing