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How We Built People Counting Software for Real-Time Visitor Tracking

  • 97% people-counting accuracy across 41 camera feeds
  • ~$180K/year saved on door-staff labor
  • <2 s from capacity breach to manager alert

Hand clickers at the door could not keep up with occupancy rules. We built people-counting software that reads existing cameras, counts visitors in and out in real time, and warns a manager before a floor gets too crowded.

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Laptop showing a live operations overview for visitor counting: current occupancy against capacity, visitors, entries and exits today, peak hour, a real-time foot traffic chart, occupancy per location and live alerts

Project Details

A US facility management company came to us mid-pandemic with a simple ask and a hard deadline. State occupancy rules capped how many people could be inside its retail stores and office buildings, and its only tool was a person with a clicker at each door. Vendors pitched visitor-tracking software, crowd-counting software, and footfall-analytics software, all tied to new sensors. The client’s brief was simpler: an automatic people counter that worked with the CCTV system already on the walls, needed no new hardware, stored the people-counting data for later, and could tell a floor manager when a space was filling up.

Client
People Counter
Industry
Operations Management
Solution
AI-powered real-time people counting software
Service
Computer Vision Development + AI/ML + QA + Deployment
Platform
Web Dashboard + Camera Integration
Scope
AI Models, Backend, Dashboard, Camera Integration, Analytics
Duration
~5 months
Location
US

Business Challenge: Accurate Visitor Counting

In spring 2020, the client counted foot traffic at its six busiest locations on paper. Two staff members stood at every main entrance with hand clickers, one for people entering and one for people leaving, and a supervisor reconciled the tallies each hour. Internal audits put the manual people counts off by roughly 28% at peak times. Side doors were not covered at all. When occupancy limits dropped to half of fire code, nobody could say with confidence how many people were in a store at a given moment.

  • Real-Time Accuracy Without Human Error

    Clicker counts drifted by 28% in busy hours, and the error grew through a shift. Groups walking in together got logged as one, kids got skipped, and the running total was stale by the time a supervisor read it. Accurate people counting had to run without a human in the loop.

  • Integration with Existing Infrastructure

    The client had a CCTV system of 41 IP cameras across six sites, from three vendors, installed over eight years. Dedicated people-counting sensors, thermal imaging, or overhead depth units were quoted at over $90,000, plus a three-month install. The software had to run on the cameras already in place.

  • Distance Monitoring Without Extra Staff

    Counting how many people enter was half the job. State guidance also asked for six feet between visitors, and nobody could monitor crowd density across 41 feeds by eye. The same system had to estimate distance and warn a manager before a queue became a crowd.

Technologies Behind the People Counting Solution

  • Python logo
  • PyTorch logo
  • OpenCV logo
  • AWS logo

Our People Counting Software Solution

One constraint shaped every decision: the cameras were staying. So instead of a sensor product, we built people counting technology that treats any RTSP stream from an existing IP camera as a counting source. A person detector fine-tuned on about 28,000 frames from the client’s own cameras picks people out from carts, strollers, and reflections, and a tracker follows each one across frames so a shopper who pauses in the doorway is not counted twice. Training data came from the camera angles the client actually had, from straight overhead to an angled entrance view, and from lighting conditions that ranged from bright storefront glass to a dim parking-level lobby. The computer vision pipeline is Python end-to-end.

Bidirectional counting happens on a virtual line drawn across each entrance. Cross it one way, the entry count goes up. Cross it back, the exit count does. Occupancy at any given moment is the difference, and the real-time data updates within a second on a dashboard any manager can open on a laptop or mobile device. Where an entrance has multiple cameras, the people counter merges their counts so a double-door lobby does not log the same visitor twice. Where an angled entrance view made the line ambiguous, we let the site admin draw a polygon zone instead, which fixed three of the six pilot locations in one afternoon.

Distance estimation runs on the same frames. Each camera goes through a one-time calibration, four points on the floor, and from then on the system converts pixel positions into floor coordinates and measures the gap between people. Anything under six feet for longer than eight seconds raises a soft flag. Repeated flags in the same zone escalate to a crowd density alert for the floor manager.

Processing splits between edge and cloud. Small edge boxes at each site handle detection on four streams each at 24 FPS, so the video footage never leaves the building. Only counts, zone events, and thumbnails go to AWS, where the analytics layer builds foot traffic reports, heat maps, occupancy data by hour, and peak-hour views from the people counting data. Nothing about the counting software depends on the camera brand, which is why three vendors’ cameras run through the same people counting system today.

  1. Bidirectional Counting on Existing Cameras

    Any existing IP camera with a view of a door becomes a people counter, with entry and exit counts per door. No new hardware, no sensor calibration, and a new camera can be added to the people counting system from the dashboard in under ten minutes.

  2. Live Occupancy Monitoring

    A running headcount per floor and per building, checked against capacity limits the client sets. At 90% of the limit, the entrance screen turns amber. At 100%, door staff get a hold signal, and the manager gets a push alert.

  3. Social Distance Estimation

    Calibrated cameras measure the distance between people on the floor to within about a foot. Zones that stay crowded for more than 30 seconds trigger an alert, and the log of those events becomes the client's compliance record.

  4. Foot Traffic Analytics and Heat Maps

    Real-time footfall analytics per entrance, hourly, daily, and weekly, plus heat maps and dwell time by zone, showing where people linger and for how long. Store teams use this data to identify peak times, plan staff scheduling, and adjust product placement to match, and once sales figures are joined in, to read conversion rate per store.

  5. Multi-Camera, Multi-Site Dashboard

    All 41 cameras and six sites in one view, with multiple cameras per site and drill-down to a single door. Role-based access means a store manager sees one location while the operations team sees every site side by side.

  6. Privacy-First Processing

    Detection happens on-site. The people counting system stores count data and anonymized zone events, not faces, and raw video clips are kept for 48 hours for QA only, then deleted.

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Agile Development Methodology

Project Journey

The five-month project ran in two-week sprints, starting with a three-week discovery phase in which we pulled sample video footage from all 41 cameras and labeled about 28,000 frames. The first working people counter went live on one entrance in sprint 3, and the client compared it against clicker counts for two weeks before we touched the rest of the sites. Ten sprints later, all six locations were live with counting, occupancy alerts, and distance monitoring.

Week sprint cycles
2
Sprints completed
10
of sprints delivered on schedule
95%
Team members
6

How People Counting Software Works

  1. Camera Stream Ingestion

    Each edge box connects to the existing IP cameras in the site's CCTV system over RTSP, pulling frames from up to four streams at 24 FPS.

  2. Person Detection

    A YOLO-based detector, fine-tuned on the client's own footage, finds every person in the frame and ignores carts, bags, and mannequins.

  3. Tracking Across Frames

    A tracker assigns each detected person an ID and follows them, so one visitor produces one count even if they stop or turn around.

  4. Line Crossing and Direction

    When a tracked person crosses the virtual line at an entrance, the direction of travel decides whether it counts as an entry or an exit.

  5. Occupancy and Distance Checks

    The running total is compared against capacity limits, and calibrated floor coordinates measure the distance between people in each zone.

  6. Dashboard, Alerts, and Reports

    Counts and events sync to the cloud within a second. Managers see live occupancy, get alerts, and pull footfall data by hour, door, or site.

Development Process Flow

Computer vision development rarely survives contact with real cameras unchanged, and this project was no exception. Shipping a working counter on one door in week six meant the client saw real numbers next to clicker tallies before we scaled, and two of those early reviews changed where the counting line sat. By the time all six sites went live, the client had signed off on ten working increments rather than one big release.

Laptop screen listing sites with a portfolio summary: total visitors, average occupancy, visitor volume by location, and a card per store, mall, office and car park lobby with occupancy against capacity

Inside Each Sprint

  • Daily Scrum

    15-min sync every morning

  • Retrospective

    Inspect & adapt process

  • Sprint Review

    Demo to stakeholders

  • Increment

    Shippable product update

How We Delivered the Software for People Counting

  1. Scope & Timeline

    We define the project goal together, agree on priority features, and set a realistic delivery date and budget.

  2. Feature Priorities

    We build a ranked list of everything the product needs, starting with what matters most to the business.

  3. Sprint Kickoff

    Work is broken into 2-week cycles. At the start of each, we select the next set of features to deliver.

  4. Development Cycle

    The team builds, tests, and integrates features throughout the sprint.

  5. Review & Feedback

    At the end of every sprint, you see working software and give feedback that shapes the next cycle.

  6. Delivery

    Each sprint produces a shippable piece of the product. We review what worked, adjust, and move forward.

Results

Before

  • 2 staff with hand clickers per main entrance, side doors uncounted
  • Manual people counts off by ~28% at peak hours, reconciled once an hour
  • No way to know occupancy at a given moment across six sites
  • Social distancing checked by walking the floor, with no record for audits
  • A CCTV system of 41 cameras recording video footage that nobody analyzed

After

  • 97% people counting accuracy against manual ground truth, at every door including side entrances
  • Live occupancy for all six sites, updated within a second, with alerts under 2 seconds
  • ~1,100 staff hours a month returned across six sites, roughly $180K a year in door-staff labor
  • Distance events logged automatically, giving the client a compliance record for the first time
  • One dashboard for counts, heat maps, peak times, and footfall data, on the same cameras as before
Laptop on a stand showing one entrance camera: people tracked with IDs crossing an in and out line, entries and exits today, current flow, detection confidence of 97% and a log of counting events

Impact of CCTV People Counting Software After Implementation

Numbers first. People counting error fell from about 28% to 3%, occupancy became a live figure rather than an hourly estimate, and the client stopped paying two people per door to hold clickers. That last line is what the finance team noticed: roughly $180,000 a year in labor moved back onto the sales floor, from a people counter that cost less than the sensor install it replaced.

The unplanned win came after the pandemic rules eased. The client kept the people counting camera software running because the footfall data turned out to be worth more than the compliance log. Store teams now make data-driven decisions: they set foot traffic against sales by hour to work out conversion rate per location, staff scheduling follows real peak hours instead of guesswork, and two sites moved their promotional displays after heat maps and dwell time showed where visitors actually stopped.

  • Accuracy
  • Operational Efficiency
  • Safety Compliance

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What's Next

The current release of the people counting software covers real-time counting, occupancy monitoring, distance estimation, and historical analytics. Candidate directions for the next phase:

  • AI-Powered Object Recognition: Teaching the people counter to tell shopping carts, strollers, and staff uniforms apart from customers, so store traffic reports separate shoppers from employees and deliveries.
  • Multi-Location Management: Portfolio views for retail operators, transportation hubs, and public buildings with dozens of sites: unlimited locations per account, passenger flow and crowd management views, cross-site benchmarks, and occupancy data pushed to property management systems so landlords can report on high traffic areas and tenant satisfaction.
Laptop showing heatmaps and spatial analytics for a store: a density heatmap over the floor plan, busiest and longest-dwell zones, visitor flow between zones and a zone performance table

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