Vehicle Detection

Overview

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, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
• Note: for those first two steps don’t forget to normalize your features and randomize a selection for training and testing.
• Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
• Run your 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.

The Project

If you have already installed the CarND Term1 Starter Kit you should be good to go! If not, you should install the starter kit to get started on this project.

Set up the CarND Term1 Starter Kit if you haven’t already.

Here is the result video :

• Project wirteup report in Writeup_report
• Code for vehicle detection is in Vehicle_Detection.ipynb
• Code for vehicle detection and combined with lane line finding is in image_pipeline.py
• Code for lane line finding in find_lanelines.py

Here are links to the labeled data 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.