Self Driving Car Engineer Project 5 - Vehicle Detection & Tracking

Benjamin Söllner, 25 Jun 2017

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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.

Files Submitted

My project includes the following files:

• writeup_report.md / .html: the writeup of this project including the showcase images (you are reading it)
• project_video_output.mp4: the output video with bounding boxes for cars identified
• project_video_output_debug.mp4: the output video with diagnostic information
• test_video_output.mp4: the short test video with bounding boxes for cars identified
• test_video_output_debug.mp4: the short test video with diagnostic information
• src/model.py: the code / algorithm to perform vehicle detection / tracking
• src/tests.py: boilerplate functions to test the model code and view the output with matplotlib
• src/hyperparams.py: the hyperparameters for the algorithm
• CarND 05 Vehicle Detection and Tracking.ipynb / ``.html```: a (rendered) iPython notebook that contains the output of all tests
• tester.py: a command line tool to test the code without python notebooks