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.
Industries
Last updated
October 8, 2021
Usage
Requires Viso Suite
Support

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.

To get started, request a personal demo, we will put together a plan that includes all the services you need to get started.

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