Vehicle Detection

Here you can find the Writeup

Files in the project

• cnn_detect_car.py Script to detect cars in frames or videos using the model.h5 which is a Convolutional neural network.
• cnn_train_classifier.py File to train the CNN classifier.
• detect_car.py Same as cnn_detect_car.py but using only computer vision methods.
• extra_function.py This is a library with some methods the program use.
• linearSVC.model This is the SVC model trained to detect cars using computer vision methods.
• model.h5 Trained model for the CNN version
• model_v2.h5 Another model for the CNN version that works differently
• no_car_generator.py Script to generate images from videos to generate more data values that doesn't contain cars on it.
• train_classifier.py Script used to train the SVC.
• writeup.md Here you can find a description of the process I took to develop this solution

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.

Some example images for testing your pipeline on single frames are located in the test_images folder. To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called ouput_images, and include them in your writeup for the project by describing what each image shows. The video called project_video.mp4 is the video your pipeline should work well on.

As an optional challenge Once you have a working pipeline for vehicle detection, add in your lane-finding algorithm from the last project to do simultaneous lane-finding and vehicle detection!

If you're feeling ambitious (also totally optional though), don't stop there! We encourage you to go out and take video of your own, and show us how you would implement this project on a new video!