Udacity Self-Driving Car Nanodegree Program - Project 5

In this project, your goal is to write a software pipeline to detect vehicles in a video (start with the test_video.mp4 and later implement on full project_video.mp4), but the main output or product we want you to create is a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup.

1. classifiers (A directory containing different classifiers)
2. test_images (A directory containing test images)
3. output_images (A directory containing generated images)
4. test_videos_output (A directory containing generated video)
5. Vehicle_Detection.html (Generated HTML format for review)
6. Vehicle_Detection.ipynb (Source code for visualization)
7. helper_functions.py (Source code)
8. README.md (Readme file)
9. writeup.md (Writeup file)
10. dict_vehicle_detection.p (Data file for saving trained classifier and corresponding parameters)
11. LICENSE (License)

1. Configure your conda environment with Udacity CarND-Term1-Starter-Kit
2. Install Open CV ·pip install opencv-python·

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.