Identify and track vehicles from a front-facing camera on a car with CV and ML techniques
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README.md

Vehicle Detection and Tracking

Identification and tracking of vehicles.

Installation

conda env create -f environment.yml
source activate environment

Usage

 Usage:
  video_pipeline.py [-i <file>] [-o <file>]
  video_pipeline.py -h | --help

Options:
  -h --help
  -i <file> --input <file>   Input text file [default: ../videos/project_video.mp4]
  -o <file> --output <file>  Output generated file [default: ../videos/project_video_output.mp4]

NB:

  • Input video needs to be a feed from centered onboard camera.
  • You will need to download the training data (vehicle and non-vehicle images) and put in a folder named data. It has been ignored for sizing issues.

Example

python video_pipeline.py-i ../videos/project_video.mp4 -o ../videos/project_video_output.mp4

alt text

Detailed description

In this project, my main goal is to write a software pipeline to identify and track vehicles in a video from a front-facing camera on a car (without any false positives).

You will find the exploration code for this project is in the IPython Notebook and a video displaying how my pipeline can allow to detect and track vehicles on the road. A more detailed report of the project is available here.

The steps of this project are the following:

  • Build a feature engineering pipeline to create a dependent variable for our classification model.
  • Train a classifier using the selected features (HOG features and color features in our case).
  • Implement a sliding window search and use the trained classifier to search for vehicles in images
  • Create a video pipeline that create bounding boxes and identify vehicles most of the time with minimal false positives.