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MV People Detection



All second generation (MVx2) cameras are capable of processing powerful analytics on the camera itself and transmitting this metadata to the Meraki cloud. This revolutionary architecture dramatically reduces the cost and complexity of gathering detailed analytics in any environment.


With this architecture, all MV cameras have the ability to transmit motion metadata to the Meraki cloud, enabling Motion Search, a powerful tool in retrieving video. This also enables Motion Heatmaps, which is part of MV advanced analytics, but will not be discussed in this document.


On the second generation cameras, one of the new and exciting capabilities of this platform is the ability to do machine learning-based analytics. With this comes people detection, which allows you to understand how people are moving through and using your physical spaces.


There are a number of features that enable you to interact with MV people detection and we will walk through them one by one in this article.

“Show People” on your Camera Feed


The first place you can see MV people detection is when viewing historical video and clicking on “Show People” on a single camera’s video page. Objects detected as people will be enclosed by yellow boxes as shown.




MV Analytics Tab


Both people detection and motion metadata are aggregated for you to analyze in the Meraki Dashboard, under the Analytics tab for each camera. Here, Meraki uses people detection analytics to help create histograms of people detected by the camera and records statistics about how many people entered or were present at a specific time. The dashboard can show you this data at a minute, hourly, or daily scale, which allows you to identify time-based trends and anomalies in the usage of your space. This tab’s information also serves as a tool to quickly find relevant video clips with histograms and time links. Motion heatmaps are also provided at the bottom of the Analytics page to correlate the people detection data with motion data.



People Detection Features
  • Time Resolution, Date, Start Hour and End Hour

    • Configure the scale for the slices of your histogram and the time range for your analytics.

  • People Detection

    • People Detection by Zones

      • If you have configured zones on your camera, you can also view the people detection analytics by zone. The zones feature is explained in more detail in the Zones section of this article.

    • Most Utilized Hour

      • This value represents the hour that had the highest average occupancy.

    • Estimated Peak Occupancy

      • This value takes the maximum of the estimated occupancy of the scene across the selected time range. The estimated occupancy of a scene is calculated every minute and is an average of the number of people detected for every second of the minute.

        • Example 1: 10 people are in a camera's field of view (FoV) for 5 seconds, followed by 0 people for the remaining 55 seconds of that minute

          • The estimated occupancy for that minute would be rounded to 1 person.

        • Example 2: 10 people are in a camera's FoV for 55 seconds, followed by 0 people for the remaining 5 seconds of that minute

          • The estimated occupancy for that minute would be rounded to 9 people.

    • Total Entrances

      • This value represents the number of entrances of objects detected as people within the scene. See definition of entrances in the next section.

  • Entrances (bar chart/histogram)

    • Presents the total number of entrances per hour/day for the hour/date range specified. This can be up to 24 hrs, 7 days or 1 hour - depending on the selected time resolution.

    • An entrance is defined as a detection of an object identified as a person. A single person may be detected as multiple entrances within the period that they are within the frame.

      • If a person walks into a camera’s FoV, walks across the frame, and then walks out unobstructed by other people or objects, the person will likely be counted as one entrance.

      • If a person walks into a camera's FoV with a column in the middle, stands behind a column for a short while, and then reappears from behind the column before exiting the frame, this person will likely be counted as two entrances to this total.

      • If a person bends down and is for a moment not detected as a person due to his/her crouched shape, and then stands up again, this person will likely be counted as two entrances as well.



You can set zones on your camera on the Zones page under your camera’s Settings tab. Use this to separate your people detection data according to areas within the camera’s field of view (FoV).




You can navigate to the Analytics tab of a camera, select which zone you want to analyze and view the people detection data only within that zone.




You can also click on the Edit option under each zone and change the sensitivity of how much a person must be in the zone to be counted.




MV Sense API

The final way to interact with MV people detection analytics is to use the API endpoints provided with MV Sense to build intelligent business solutions. Read the MV Sense article for more information.

Example Use Cases

Here are some examples of what you can do with people detection analytics.

Example 1: Find anomalies

Say that one day on your histogram shows people detection during a time in the day when you would not expect anyone to be present (for example, 4am on a weekday). By clicking on that hour in the histogram, you can immediately see what was happening during that time (for example, someone grabbing coffee, likely to prepare for his very early 4am meeting).



Example 2: Find when the space was most occupied

By clicking on the number under ‘Peak Occupancy”, you can go straight to the video clip when the highest occupancy was observed, and get more insights about what was going on.

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Technical FAQs

How does it work?

Software on the camera analyzes images multiple times per second and identifies where people are located. The camera then tracks the location of these people over time to understand when they came, stayed, and left. The camera rolls up its findings and reports them to the dashboard, where you can view the data in a summary form. Our object detection is driven by computer vision and machine learning.

What do you mean by "machine learning"?

Meraki smart cameras use deep learning, a type of machine learning at the forefront of artificial intelligence research, to drive our computer vision object detection. The smart camera development teams continually show  a computer thousands of examples of what people look like and it "learns" how to identify them more and more accurately over time. The model improves as we provide it with additional training data.


Right now the detector is trained on data from a small number of cameras. The smart camera development team expects to deploy a lot more of these second generation cameras, and plans to use data from opted-in customers and all of their diverse deployment scenarios to continually make the model better.

Does this do person identification or facial recognition?

No. Meraki smart cameras don't identify or track specific individuals, they just detect them.

Does this take any extra bandwidth? Do I need another server?

No! All of the image processing is done right on the camera's processor. Like all Meraki products, the hardware sends small amounts of metadata back to the dashboard for further processing and storage.

Why am I not getting the numbers I would expect?

Once a camera is physically installed in a proper location and adjusted for the correct FoV, the MV will automatically start to gather analytics data. For more information on installing a MV camera to optimize people detection performance, see the deployment guidelines below. In any deployment, you should use the data comparatively for observing trends and anomalies, as opposed to using it as absolute measurement. Refer to the People Detection Features > Entrances section above for some more explanation.

How can I troubleshoot MV people detection?

There are no public facing logs to troubleshoot the analytics tools. You can use the "Show People” tool when viewing historical footage to get a rough idea of what the MV people detection model is detecting.

Deployment Guidelines for MV People Detection

This document details guidelines for installation of cameras with on-board people detection in retail and security use cases.

Cameras should be installed wherever there is a need to gather enhanced consumer behavior insights and information about activity that has occurred or is occurring at a given location.

Common Deployment Locations

MV people detection commonly used in areas with high foot traffic and will work well in areas with little obstruction and where the line of sight distance is at least 5 feet. Some examples include:

  • Entrances, exits leading inside and outside of a building

  • Staircases and other walkway intersections

  • Areas where people stand (e.g. clothing racks, promotional stands)

  • Queues (e.g. checkout lines)

For optimal performance of people detection, consider the guidelines below during both pre-installation and installation.

Common Deployment Challenges

Consider the following challenges when choosing how to deploy your MV for people detection.

  • First, understand that changes in lighting will affect the effectiveness of the people detection. When placing the camera by entrances or exits, try to place the camera indoors where consistent, even lighting can be better controlled.

  • Avoid pointing the camera out towards a street or walkway outside of the location of interest. This deployment might incorrectly capture people walking outside the store or into another store. This can also be mitigated by creating zones to narrow down the people detection data to a certain part of the frame.

  • Avoid angling the camera such that many people occlude/obscure others. For example, a camera will have more trouble if it is looking at a queue straight on as opposed to from the side.

  • If a camera is capturing an area where multiple walkways intersect (such as the end of an aisle), the camera should capture as much of the whole intersection as possible to maximize the duration of time that a single person is seen on the screen.

  • Avoid installing a camera where glare off other objects (like glass) may be present at various times throughout the day.

  • Avoid aiming the camera at highly reflective surfaces (like mirrors).

  • Avoid deploying the camera in a scenario with objects that appear human-like (e.g. displays showing video of people walking, posters depicting people, mannequins).

Installation Guidelines

  1. Before installing any cameras, identify the best deployment locations given the guidelines above to ensure the camera is viewing the best scene for people detection.

  2. Now that you have optimized your deployment locations for people detection, read this chapter of Designing Meraki MV Security Camera Solutions for how to conduct a proper site survey. The site survey will help determine the best locations to install your cameras.

  3. Before permanent installation, it is highly recommended to temporarily affix the camera or camera-mount assembly, turn on the camera, and view the video stream for best results. Adjust according to the guidelines below.

    • Ensure your line of sight distance to expected foot traffic is at least 5 feet. For the MV12 fixed lens, ensure that this line of sight also does not exceed 40 feet. Maximum distance for varifocal lenses will be different and should first be tested based on the optical zoom applied.

    • Ensure that the camera is mounted at a height of at least 6 feet above ground. Performance typically improves when mounting at or above 10 feet.

    • Ensure that the camera is angled such that the desired scene is free of or mitigates obstructions.

    • Ensure that the camera’s optical zoom (if applicable), sensor crop and focus are set optimally. Follow these articles on Adjusting the Field of View of MV22 and MV72 cameras and Focusing MV22/72 and MV21/71 Cameras

    • Double check that you have proper lighting in the area and high dynamic range (if needed) and night mode are set according to your needs.

  4. Have someone walk around and observe that object detection is working as expected. If not, repeat step 3 until the desired results are obtained.

  5. Once satisfied with the performance of people detection, permanently install the cameras.

If the deployment is consistent with these deployment guidelines, the MV is expected to be able to detect objects accurately and provide people detection results that can be used to make informed decisions. If deployed well, the MV analytics are much more reflective of the ground truth and should be used quantitatively over an extended period of time with a high enough volume of people to observe trends and anomalies.


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