People Counting

Area-based people counting in real-time using common surveillance cameras.

What Is People Counting With Deep Learning?

People counting with computer vision uses deep learning algorithms to detect and track individual people in the real-time video of common, inexpensive surveillance cameras. Novel deep learning algorithms provide high accuracy in both indoor and outdoor scenes.

Automatic people detection and counting in real-time video streams are important in intelligent video surveillance. Hence, automated people counting with cameras is a key application in smart cities. It also helps businesses to analyze customer traffic in indoor and outdoor scenes.

Counting of people in stores has recently gained popularity due to COVID-19 measures to prevent the spreading of the coronavirus. Another popular use case is the counting of queuing people.

Key Features of People Counting

Computer Vision using surveillance cameras is a highly scalable approach to accurately and consistently count people across a high number of locations.

  • People detection with deep learning models to detect humans and their trajectory.
  • Define regions of interest within the camera image to focus the people detector (exits, entrances, queuing areas).
  • High-performance with deep neural networks to count people in complex, crowded spaces.
  • Edge Computer Vision allows on-device machine learning with local image processing to guarantee privacy.

Value of Vision-Based People Counting

Deep learning based footfall counting systems achieve high accuracy with minimal hardware requirements.

  • Automatic and contactless people counting without the need for physical tracking devices, costly installation, and maintenance.
  • Common surveillance cameras can be used for people counting, making the method comparably easy to implement, even in large-scale use cases.
  • Increased safety of customers and workforce by ensuring compliance with government regulations, for example, related to COVID-19 measures.
  • Actionable insights to estimate the number of people in retail stores in real-time, discover peak hours, bottlenecks and compare key metrics across different locations.
  • Leverage insights by sending data to third-party systems and visualizing it in dashboards.
Last updated
October 8, 2021
Requires Viso Suite

How to get started

This solution is built on Viso Suite, the all-in-one computer vision platform to deploy, run and scale the solution with powerful tools and infrastructure.

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