Protect & respond · Feature guide

Human and vehicle detection CCTV

A moving branch should not demand the same attention as a person at your gate. Human and vehicle detection classifies supported objects so you can decide which movement matters. It is often a practical first step when ordinary motion alerts become too noisy.

Illustration distinguishing a person and vehicle while leaving a dog unboxed
AI-generated illustration of human & vehicle classification. The scene and overlays explain the concept; they are not a PGAK screenshot or measured result.

How it works

Filter activity by the type of object detected instead of treating every movement as an incident.

Where it could help your business

Focus a commercial entrance’s alerts on people and vehicles.

Test a normal working day as well as quiet hours. Include headlights, rain, shadows and people carrying large objects. Review missed events alongside false alerts; making a system quieter is not helpful if it also stops reporting activity you need to see.

What your camera setup needs

Detection depends on object size, angle and lighting. Classification reduces noise; it does not eliminate errors.

Does human detection remove all false alarms?

No. Small subjects, poor lighting and unusual camera angles can confuse classification. Good installation and testing matter as much as the feature name on the box.

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 · C06

Object classification: 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.

Animals, headlights, swaying trees — events no one needed to see.

Share of irrelevant events the classifier removed in your own test.

Spot-checking what was filtered out, so real events are not hidden.

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 = irrelevant events × filtering fraction × review minutes ÷ 60 − error-review hours

Worked example (illustrative inputs)

600 events; 0.5 filtered; 1 minute; 1 review hour.

staff time released: 4 hours a month.

Formula version 1.0.0. The full standalone tool: false alarm cost.

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.