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AI detection · Motion detection

Motion detection

Motion detection uses background-subtraction analysis to identify meaningful movement in the camera view while filtering out camera noise, lighting shifts, and environmental changes. Unlike legacy pixel-based motion detection, which generates an estimated 90% false alarm rate across the industry, Camzify applies intelligent filtering to separate real activity from noise.

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  • Vehicles entering a driveway or parking area after hours
  • People walking through a loading zone during closed hours
  • Package or equipment movement in a storage area
Camzify motion detection zone configuration showing a shipment truck flagged inside a drawn detection zone at a warehouse entry point

This capability detects and alerts on:

  • • Vehicles entering a driveway or parking area after hours
  • • People walking through a loading zone during closed hours
  • • Package or equipment movement in a storage area
  • • Any activity in a room that should be empty overnight
  • • Movement near an entrance during non-business hours
  • • Sudden activity in an area with no scheduled foot traffic

Why motion detection matters

Every camera-based security system starts with the same basic question: did something change in this frame? Naive pixel-difference motion detection answers that question too literally, a passing cloud shadow, a fluttering flag, autoexposure hunting in low light, or a spider building a web in front of the lens all register as "motion" exactly the same as a person walking through frame.

That literalism is why legacy motion detection has a reputation for flooding operators with alerts nobody trusts. Once an alert feed is mostly noise, people stop looking at it, and the one alert that mattered gets lost in the pile with the hundreds that didn't.

Camzify's motion detection separates the two by modeling what "normal" looks like for a scene and only flagging genuine deviations, a foreground object entering a background that has been established as static, before that candidate is handed off to object detection and tracking for confirmation.

Diagram showing background subtraction isolating a moving foreground object from a static modeled background
Background model

How it works

Background subtraction

The engine continuously builds a statistical model of the static background for every camera feed. Regions of the frame are flagged only where pixels deviate from that model beyond a threshold, not simply anywhere the raw image differs from one frame to the next.

Noise filtering

Candidate motion regions are filtered against known sources of noise, lighting transitions, camera auto-exposure adjustments, compression artifacts, and small repetitive movement like foliage in wind, before being passed on. Motion candidates that survive filtering are handed to multi-object tracking for object confirmation, which is what ultimately determines whether an alert fires.

Alert delivery

Confirmed motion events include a timestamp, the affected region of the frame, and a snapshot. They route through the notification queue alongside every other alert type, with the same acknowledgment and false-positive marking workflow.

Configuration

Motion detection is tuned per camera in the configuration panel. Each camera supports:

  • • Sensitivity set to Low, Medium, High or a custom level, to match scene complexity
  • • Inclusion and exclusion zones, so a tree line or a public pavement is never assessed at all
  • • A minimum object size threshold, to ignore movement below the size you care about
  • • An object filter, so a camera alerts only on Person, Vehicle or Animal rather than on any motion
  • • Notification window per camera, e.g. notify after hours only

These stack. A loading yard that alerts all night on foliage usually needs an exclusion zone over the tree line and an object filter set to Person and Vehicle — after which the same camera reports the arrivals that matter and stays quiet for everything else.

Configuration panel showing motion sensitivity, masked regions, and schedule controls for a camera feed
Motion sensitivity
Facility view showing motion detection masked zones covering entrances and storage areas during closed hours
After-hours monitoring

Common scenarios

  • • A warehouse floor that should show zero activity between closing and the morning shift
  • • A parking lot where vehicle movement after hours warrants a check
  • • A rooftop or utility area masked to ignore blowing debris but still catch a person
  • • A retail storeroom monitored for movement outside stocking hours
  • • A construction site where any nighttime movement is worth a look
  • • A back office corridor where motion outside business hours is unexpected

In a patrol round

During a virtual patrol round, alerts from this detection model contribute to the compliance assessment at each camera stop and are logged in the patrol report.

Related

All AI FeaturesVirtual PatrollingWarehousesRetailPerimeter SecurityPricing
FAQ

Frequently asked questions

Motion detection flags meaningful movement anywhere in the frame using background-subtraction analysis, without evaluating a specific line or zone. Line and zone intrusion detection go a step further, requiring a confirmed object track to cross a defined boundary or enter a defined area before firing.

No. Motion detection identifies candidate regions of meaningful change in the frame. Those candidates are then handed off to multi-object tracking, which confirms whether the movement corresponds to an actual person, vehicle, or other object before an alert is generated.

Instead of comparing raw pixel values frame to frame, the system models what the static background of a scene normally looks like and only flags regions that deviate from that model, filtering out lighting transitions, auto-exposure changes, and small repetitive movement like foliage in wind.

Yes. Each camera carries an object filter that restricts alerts to Person, Vehicle or Animal, so a car park camera can ignore a cat crossing at 3am while still reporting a person on foot. It works alongside the sensitivity setting and the minimum object size threshold rather than replacing them.

An inclusion zone limits assessment to the area you draw, so everything outside it is ignored. An exclusion zone does the reverse: the rest of the frame is assessed and the area you draw is left out. Most false-alarm problems are solved with an exclusion zone over the specific thing that moves and does not matter, a tree line, a public pavement, a flapping banner.

Yes. Masked regions can be drawn over areas like public streets, tree lines, or reflective surfaces that would otherwise generate motion candidates, so the system only evaluates the parts of the frame that matter.

Motion detection is built around a modeled static background, so it performs best on fixed camera views. A PTZ camera that is actively panning or zooming will temporarily invalidate the background model until it settles on a new fixed position.

A confirmed motion event includes a timestamp, the affected region of the frame, and a snapshot, and routes through the notification queue with the same acknowledgment and false-positive marking workflow as every other alert type.

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