Project OverWatch

About the Project

Project OverWatch aims at providing realtime detection of accident from live CCTV footage. This project aims at helping government identify accidents in public roads and alert the police and concerend authorities, so that the medical care can be provided,if needed. It also aims at helping out solving hit-and-run cases,as it will record every accident scenerio.

This Project is made with the help of Microsoft Lobe. [Lobe](http://lobe.ai/) is a free, easy to use app that has everything you need to bring your machine learning ideas to life.

Web Bootstrap takes the machine learning model created in Lobe, and adds it to a project in the browser that uses [React](https://reactjs.org), [Create React App](https://github.com/facebook/create-react-app), [TypeScript](https://www.typescriptlang.org/), and [TensorFlow.js](https://www.tensorflow.org/js).

Status of the Project

This project is currently work in progress.

Achieved features are :

1. Accident Detection using ML
2. Integration of tensorflow code with React

Features need to be developed:

1. A UI for easy navigation
2. An overlay system for statistics

Get Started

1. Clone or download the project on your computer and install Yarn. Yarn is the software package that will install all the dependencies and make sure the code automatically reloads when changes are made.

2. Run yarn install to install required dependencies and run yarn start to start the server in development mode. This will open a web browser to localhost:3000. By default, this project is using the TensorFlow.js exported model from Lobe found in the public/model/ folder.

3. To use your own model file, open your Lobe project, go to the Use tab, select Export, and click on the TensorFlow.js model file. When exported, drag the model.json, signature.json, and all the *.bin files to the public/model/ folder.

Additional Information

Check out the Create React App documentation for more information on React and the project structure.

There are three main components for the ML part : Camera, Prediction, and StaticImage.

1. The Camera, which runs in components/camera/Camera.tsx is responsible for displaying a live full screen view of the user's selected webcam.
2. The Prediction component components/prediction/Prediction.tsx is the box in the lower left hand corner, and is responsible for displaying the top prediction results and their confidences.
3. The StaticImage component components/staticImage/StaticImage.tsx displays an image selected from the file picker and runs it through the model from a canvas element.

Known Issues

TensorFlow.js on Safari may have problems initializing the WebGL backend for acceleration and will fall back to the CPU. You can use the WebAssembly (wasm) backend as an alternative to WebGL: https://www.tensorflow.org/js/guide/platform_environment#wasm_backend