Skip to content

Latest commit

 

History

History
48 lines (35 loc) · 1.55 KB

README.md

File metadata and controls

48 lines (35 loc) · 1.55 KB
Raw

Real-time Road Traffic Surveillance using Cloud

FlowChart

Ivy

Ivy is an open source video-based vehicle counting system which employs several computer vision techniques to detect, track and count vehicles in a traffic scene.

Output

Requirements

  • Python 3
  • AWS account

AWS Services to be setup:

  • EC2
  • Lambda
  • Media Convert
  • S3
  • Cloudfront

Setup

  • Clone this repo
  • Install the dependencies in requirements.txt pip install -r requirements.txt.
  • Install detector YOLO's weights and place the content file in the data/detectors/yolo directory.
Detector Description
yolo Perform detection using models created with the YOLO (You Only Look Once) neural net. https://pjreddie.com/darknet/yolo/

Run(Only for testing ivy model locally)

  • Create a .env file (based on .env.example) in the project's root directory and edit as appropriate.
  • Run python -m main.

Run(Cloud vehicle counting)

  • Create instances mentioned above.
  • Replace bucket names with your bucket name.
  • Change the path of the local video file.
  • Start the server by running python Run.py.
  • Start the consumer on the cloud by running python consumer.py.
  • Start the producer on the local by running python producer.py.
  • Run the codes in Lamda to setup the file conversion and log writting.
  • Setup the web application by running the HTML on the web browser.
  • Refresh the webapp to see the output.