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AI CCTV scene change detection

Sometimes the important event is something left behind. Scene-change detection compares a monitored area with a learnt reference and flags a difference. A warehouse team might use it to review cartons appearing in a normally clear passage.

Illustration comparing a clear warehouse exit with cartons newly blocking the same corridor
AI-generated illustration of scene-change detection. The scene and overlays explain the concept; they are not a PGAK screenshot or measured result.

How it works

Learn a reference scene and flag changes to a monitored condition for review.

Where it could help your business

Check whether a designated factory area differs from its normal state.

Choose a scene that has a meaningful normal state. Consider cleaning, scheduled stock movement and lighting changes before deciding what counts as an exception. Test how the system resets after the area returns to normal.

What your camera setup needs

Define the normal condition and test lighting changes. This does not infer intent or understand every incident.

Is scene-change detection the same as camera tampering detection?

No. Scene change looks for a changed condition within the view. Tampering detection typically concerns a blocked, redirected or otherwise disrupted camera view.

Check it on your own site

Bring a camera model list and explain the event you want to find or detect. We can use that to scope a compatibility assessment. Ask for a written proposal naming the supported functions, processing hardware, licences and pilot checks before agreeing to an installation.

Check my camera setup →

Calculator · C16

Scene-change and tamper detection: estimate it for your site

Use your own measurements. The result is the staff time released, shown separately from any cash, using the formula below. It runs in your browser and nothing is sent anywhere.

Fields start empty. A blank is “not entered”, not zero; type 0 where 0 is true.

Money (optional)

Salary plus benefits and overheads, per working hour. Needed only if you enter a realisation above 0%.

0% unless you can name the mechanism — overtime that stops, a post not refilled, a contract reduced.

Only from your own records. May be negative.

Licences, support, connectivity. Enter 0 if there is none.

Hardware, installation, commissioning. Enter 0 if there is none.

Enter every amount on the same basis — all including GST or all excluding it. Nothing here adds or removes tax. Values are nominal rupees, not discounted.

Result

Enter your own figures, or load the worked example, to see the result.

How it is calculated

hours = cameras × checks per day × days × minutes per check ÷ 60 − exception hours

Worked example (illustrative inputs)

20 cameras; 1 check; 26 days; 0.5 minutes; 1 exception hour.

staff time released: 3.333 hours a month.

Formula version 1.0.0.

What this does not tell you

  • Hours released are capacity, not cash. They become cash only through a mechanism you state — overtime that stops, a post that is not refilled — entered as the realisation fraction.
  • The 'after' values are your assumptions or pilot measurements. PGAK publishes no effectiveness figure for this feature.

Evidence status: Supplier/integration or marketed capability; PGAK availability and site performance require verification.

Request a site-specific assessment with these assumptions

Your result stays on this page either way. If you want it checked against your cameras and quotation, leave a number; mention the figures on the call. Nothing you typed into the calculator is sent with this form.

Request a camera assessment. Timing and scope agreed on the call. By submitting, you ask PGAK to contact you about this enquiry. Privacy notice.

Explore related AI camera features

People counting & occupancyRead the guide ↗Queue & crowd analyticsRead the guide ↗PPE detectionRead the guide ↗
← All 24 AI CCTV features