car-speed-detection 1.7.0
Camera-based Car Speed Detection for Autonomous Driving
License: MIT
Keywords: python,car speed detection,software-based speedometer,dashboard camera,optical flow,machine learning,keras

# Camera-based Car Speed Detection for Autonomous Driving
![Using our library with optical flow to detect the speed for the car](https://github.com/ShaoChiehLien/Car-Speed-Detection/blob/main/GIFforReadme.gif)

Car-Speed-Detection provides a python library to detect the speed of the driving 
car itself by the video stream from the dashboard camera installed on the car.

Car-Speed-Detection separates the speed detection process into three steps, 
preprocessing, training, and speed detection. By using Gunnar-Farneback optical flow
algorithm along with the pipeline we developed, we are able to extract each frame into
a small size matrix depends on developers preference. We use the Artifitial Neural 
Network (ANN) to train our model with the preprocessed matrix acquired from preprocessing
function. Developers could use the trained model to detect the speed of the car
at each frame using our speed detection function.

## Getting Started
### Installation
Car-Speed-Detection is available on [PyPI](https://pypi.org/project/car-speed-detection/) and can be
installed via [`pip`](https://pypi.org/project/pip/). See
[car-speed-detection.readthedocs.io](https://car-speed-detection.readthedocs.io/en/latest/)
to learn about the API and Q&A of our library.

```shell
pip install car-speed-detection
```

### Read, Preprocess, Train, and Detect the Car Speed
The [Car-Speed-Detection](https://pypi.org/project/car-speed-detection/) library consists of the
following parts:
- Read (Read the mp4 video and output each frame into a designated directory)
- Preprocess (Preprocess each frame and output a feature set for training)
- Train (Train the model using the feature set and Artifitial Neural Network)
- Speed Detection (Detect the speed using the model and video)

## API and Example Code
Take a look at the [API](https://car-speed-detection.readthedocs.io/en/latest/API.html#) to know more about
the Application Programming Interface and [Sample](https://car-speed-detection.readthedocs.io/en/latest/Example%20Code.html) for further information on how to use our library.

## Result
In our [example code](https://car-speed-detection.readthedocs.io/en/latest/Example%20Code.html), we are able to train the model with MSE error less than 2 using the training video provided by comma.ai. We separate the video into 75% for training and 25% for testing so the result woud
be fair. The ANN model has also substaintially small amount of parameters (< 45,000), which yeild a lower latency 
compare to [other solutions](https://ucladatares.medium.com/predicting-speed-from-video-frames-dissecting-the-comma-ai-challenge-5da697b55886).

## Bugs Report
Issues and bugs can be reported by emailing lienshaochieh@gmail.com

At a minimum, the report must contain the following:
* Description of the program.
* Expected Result.
* Actual Result.
* Steps to reproduce the issue.

Please do not use the GitHub issue tracker to submit bugs reports. The
issue tracker is intended to make feature requests.

## Acknowledge
This project is managed by Shao-Chieh Lien, the software architect and student at Purdue University.

This was a senior design project at Purdue University. Christopher Crocker was in charge of generating the training data using Carla simulator that could be seen from
[this Github repository](https://github.com/CrockerC/carla_recording) and Data Section. Meenakshi was in charge or paper writing and documentation.

Special thanks to Professor Santiago Torres-Arias at Purdue ECE department for his guidance throughout our whole software development cycle!