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

2. Build the Docker image

docker build -t crowddetect .

3. 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

2. Install the dependencies

npm install

3. 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