This project was developed from two projects completed during Term 1 of the Udacity Self-Driving Car Nanodegree. It develops a software pipeline to detect vehicles and lane lines in a video.

The goals / steps of this project are the following:

For lane line detection:

• Compute the camera calibration matrix and distortion coefficients given a set of chessboard images
• Apply a distortion correction to raw images
• Use color transforms to create a thresholded binary image
• Apply a perspective transform to rectify binary image ("birds-eye view")
• Detect lane pixels and fit to find the lane boundary
• Warp the detected lane boundaries back onto the original image
• Output visual display of the lane boundaries as a video stream

For vehicle detection:

• Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a Linear SVM classifier
• Implement a sliding-window technique and use trained classifier to search for vehicles in images
• Run pipeline on a video stream 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 is an example of the pipeline output:

• VehicleDetectionLaneLines.py contains the main pipeline for training the dataset and processing the video to overlay the found vehicle windows and lane boundries.
• VehicleDetectionUtils.py contains the functions used to extract features from images to train and test the dataset, in addition, functions to determine the windows and search windows for vehicles.
• FindLaneLineUtils.py contains the functions used to warp the image to an overhead view, find lane line pixels, and determine polynomials that represent the lane line boundries.
• Threshold.py contains function for thresholding the image for lane line detection.
• CalibrateCamera.py contains the function for calibrating the camera.
• ObjectTracker.py contains class used to track and average the vehicle detection boxes and lane lines over time to more accurately display the results.

Here's a link to my video result

Here are links to the labeled data used in this project for vehicle and non-vehicle examples to train the 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. Samples from the Udacity labeled dataset were also used to augment the training data.

The images used in the Camera Calibration can also be found here.