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CrowdControl

CrowdControl is a crowd number estimation tool based on machine learning through a multi-column convolutional neural network (MCNN) model. It consists of a server and a client.

Features

  • Monitor passenger numbers using cameras
  • Provide real-time crowd estimation
  • Alert station managers and the public of potential overcrowding issues

Demo

Click here to see a demo of the client without installing anything.

Future Work

  • Refining ML algorithms and collecting more data to enhance accuracy and efficiency
  • Incorporating historical data into prediction algorithms for forecasting potential crowd levels
  • Adding a real-time vehicle tracker and public transport schedule to enable commuters to plan their travel routes
  • Exploring the use of sensors and Wi-Fi tracking for crowd counting
  • Collaborating with transportation authorities to integrate the solution with existing public transportation infrastructure
  • Extending the solution to other domains such as retail stores, museums, and events.

Installation (Server)

Prerequisites

  1. Docker

Steps

  1. Clone the repository
git clone https://github.com/genesis331/crowddetect.git
  1. Build the Docker image
docker build -t crowddetect .
  1. Run the Docker image
docker run -p 5000:5000 --env-file=.env crowddetect

Installation (Client)

Prerequisites

  1. NodeJS + NPM

Steps

  1. Clone the repository
git clone https://github.com/genesis331/crowddetect.git
  1. Install the dependencies
npm install
  1. Run the client
npm run dev

Project Members (alphabetical order)

  • Cheah Zixu
  • H’ng Cherng Khai
  • Lim Hui Ern
  • Lim Jun Yi
  • Vanessa Jing Taing