Crowd Analytics is an area where getting data in real time is a gift. No wonder nowadays, it is the most active-oriented research and trendy topic in computer vision. Traditionally, three processing steps involve in crowd analysis, and these include pre-processing, object detection and event/behaviour recognition. However many existing applications do not process in real time. Our objective was to create a unique image processing application which is completely automated, needs no human intervention, is cost effective and at times better than human performance if not as good as it, which the present applications fail to do.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Minimum Specs: Quad-Core CPU, 16GB RAM, High Resolution Webcam.
Recommended Specs: 6/8 Core CPU, 32GB RAM, Camera -DSLR with high resolution, and better pixel count.
Software Requirements:
OS: Any OS will do (preferred windows)
Python (Anaconda distibution): Should have the ability to install dependencies via pip/ conda
Node.js (version >= 8.9.3: Should have the ability to install dependencies via npm
MongoDB: MongoDB comunity server will be enough.\
Follow the below steps to run the code in your machines.\
1.First you need to create a conda environment and install the dependencies. Find the environment.yml file in the repo and run to install the dependencies. https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file.
Refer the above link to set up the environment for all the dependencies to work.
2.Then download the zip file.
3.Go into the folder.
4.Do npm install.
5.And then npm start.
6.Then once you see server is up.
7.Go to browser and localhost:3000/dashboard.\
- Prathyush Potluri
- Rohan Sukumaran
- Rutvik Vijjali
- Srijan Vasa Reddy