carnd-vehicle-detection's Introduction

Vehicle Detection

Installation and Environment Setup

Virtualenv is suggested in the environment setup of this project. Take Ubuntu for example

sudo apt-get install python-pip python-dev build-essential 
sudo pip install --upgrade pip 
sudo pip install --upgrade virtualenv

The required package has been set up at setup.py , can be installed simply by

pip install -e .

Training

Download and unzip the datasets vehicle and non-vehicle

Train an SVM classifier on the datasets

python training.py

The script generates two files svc.pkl and X_scaler.pkl

Detection

Require the source video test_video.mp4

python vehicle_detection_test.py

After executing this script, the detection result can be read on output/project_video.mp4

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, 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.

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.

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