Guide · False alarms
False alarms: real causes, real fixes
If your outdoor alarm keeps going off falsely, you are not alone. A false alarm is not an annoyance — it is the reason people switch their system off and pay for zero protection. Here are the six usual causes and what eliminates each one.
The six causes, and their fixes
| Cause | Why it happens | What fixes it |
|---|---|---|
| Vegetation and wind | The detector sees “motion”, not objects | The radar’s swaying-object filter · AI classification |
| Dog, cat, wildlife | Declared immunity is limited and mounting-dependent | Animal category on the AI camera · LiDAR size thresholds |
| Wild boar | Exceeds any “pet immunity” | Image classification — recorded, not woken by |
| Shadows and headlights | The camera reads light changes | Radar (blind to light) + AI level 1 as a minimum |
| Rain and insects | Drops and bugs in front of the lens | Radar immune · analytics rules · maintenance |
| Wrong mounting | Height and angle cancel the sensor geometry | Measured re-siting, documented in the dossier |
The rule that governs everything
Two layers that must agree. Detection (radar, LiDAR, beams) triggers; image verification confirms it is a person; the alert only fires when both agree within a time window. One layer forces a choice between missing events and living with false alarms — with two, you choose neither. The technical detail is on the systems page.
Frequently asked questions
Why does my alarm go off by itself at night?
The typical night causes are animals (cat, wild boar), wind-blown vegetation, car headlights and shadows. Each has a different fix; identifying which is yours is the first step.
How do I stop the alarm going off with my dog?
With image classification and an animal category, and perimeter detection that filters by size (LiDAR) or classifies (radar). The dog's event is recorded but does not alert you.
Wind-blown plants trigger my camera — what do I do?
Radar with the “swaying objects” filter ignores trees and bushes; on cameras, AI analytics discards vegetation movement. It is one of the commonest cases in a Mediterranean garden.
Do wild boar set off the alarm?
Yes, and they exceed any “pet immunity”. The only serious technical answer is image classification with an animal category, which records the event without waking you.
Why do shadows and headlights trigger the camera?
Because a motion detector reacts to light changes. Radar does not see light (it detects mass), and level-1 AI discards shadows and lighting changes.
Can rain cause false alarms?
On cameras and some detectors, yes: drops and insects in front of the lens. Radar is immune, and analytics rules plus maintenance reduce the rest.
How many false alarms are acceptable?
The target is close to zero: a system that cries wolf ends up switched off, and a switched-off system protects nobody. That is why post-installation tuning is part of the job, not an extra.
Can they be eliminated without replacing the whole system?
Often yes: re-siting and adjusting detector heights, applying filters and, if needed, adding an AI analytics appliance on the existing cameras. We start with a survey.
Does detectors' pet immunity actually work?
Within limits: it is a declared immunity up to a certain weight (typically up to around 25 kg) and depends critically on mounting height and angle. A bad mounting cancels it.
Why does my new detector still misfire?
Almost always the mounting: raising the sensor for looks cancels the geometry that gives it immunity. That is why we document mounting height in the dossier.
Can an insect on the camera trigger the alarm?
Yes: a spider or insect on the lens generates alerts. A classic case, solved with proper analytics and maintenance.
What is image verification?
That the alert is only raised when detection coincides with an image confirming it is a person. It is what separates a reliable alarm from one that cries wolf.
Does raising sensitivity help avoid missed events?
It is the classic trap: raising sensitivity multiplies false alarms. The fix is not more sensitivity but better classification — detect and verify separately.
Does a well-built perimeter system give false alarms?
Very few: the agreement of two layers (detection + verification) within a time window eliminates most. The technical logic is on systems.
How do I find the cause of my false alarms?
With a technical survey: we review the event history, the site and the mounting, and tell you in writing what is causing them and what is worth keeping.
Why does my outdoor alarm keep going off with nobody there?
The usual causes, by frequency: vegetation moving in the wind, animals (from cats to wild boar), shadows and abrupt light changes, car headlights, heavy rain, and insects sitting on the camera or detector. Each has a different fix — so the first step is identifying which one is yours.
Is there a security system that works with my dog?
Yes, on two conditions: image classification with an animal category (the event is recorded but raises no alert), and a perimeter layer that filters by size or classifies — LiDAR sets thresholds in centimetres per zone; radar classifies person/vehicle and filters small objects.
Does the “pet immunity” on detectors actually work?
Within limits: it is a declared immunity up to a certain weight — typically up to around 25 kg — and it depends critically on mounting height and angle. A wild boar exceeds it by far, and an “aesthetic” mounting at 2 metres cancels the geometry that makes it work at all.
What about wild boar? They come into our urbanisation at night.
A documented provincial reality: in the veterinary college study of captured urban boar, 40% came from Marbella, 30% from Málaga city, 22.5% from Fuengirola and 7.5% from Benahavís. Neither PIR nor radar solves it alone — the technical answer is image classification with an animal category, which records the event without waking you.
How many false alarms are “normal”?
As many as you will tolerate before switching the system off — in other words, the real target is close to zero. A system switched off in frustration protects exactly as much as no system; that is why post-installation tuning is not an extra, it is the job.
My current system cries wolf. Can it be fixed without replacing everything?
Often, yes: correct detector heights and positions, vegetation filters, properly configured analytics rules and, if needed, an AI appliance on top of your existing cameras. We start with a survey and a written report; whatever is good stays.
The alert
What happens, exactly, when the alert fires
A good system is not judged by what it does at rest, but by what happens in the ten seconds after a real detection. The moment two layers agree there is a person where there should not be, the sequence starts: an alert to your phone with the image of that moment, a recording of the zone, and — if you choose — active deterrence with lighting and siren.
You get a photo, not an anonymous buzz: you see whether it is the courier, the gardener or someone you don't recognise, and act with judgement instead of panic. In your area, where many homes are watched from a distance, that image is the difference between calling for nothing and calling with reason.
And the recording stays: an image obtained correctly, framed within your property, is usable evidence. The system does not just scare the intruder — it documents what happened.
Data protection
Cameras that obey the law, not dodge it
A camera that sees beyond your fence can become a legal problem instead of protection. Spanish data-protection rules (LOPDGDD, art. 22) are clear: a visible information sign, a frame limited to your property and access points, and image retention of one month at most unless required otherwise.
We treat this as part of the project, not as paperwork afterwards. We minimise the field of view to your plot, place the mandatory signage and set retention to the legal limit. In your area, where many plots border the street or a neighbour, framing well from the start avoids the complaints that year after year rank among the most frequent before the data-protection agency.
Recording well is also recording within the law: an image obtained correctly is usable; one obtained by intruding on a neighbour can turn against you.
Zoning
Zoning: making an alarm say where
A house protected as a single block can only say “something is happening”. A house divided into zones says “someone is on the north line, by the pool”, and that difference is what lets you decide in seconds instead of stepping out blind.
We divide the your area plot into logical zones — perimeter, garden, access points, the immediate surround of the house — and each has its own rule: what is watched, at what hour, at what sensitivity. At night the outer perimeter can be armed while you move around inside; when away, everything. The same installation protects differently depending on the moment.
Zoning also narrows the alert: instead of one sensor tripping on everything, each zone reports separately, and the image tells you exactly where to look.
Deterrence
Deterring beats recording the burglar
The best incident is the one that never happens. An intruder sizing up a house looks for the easy way in, and a plot that visibly detects, lights up and reacts is a plot that gets skipped for the next one. Visible deterrence does more work than any recording after the fact.
In your area we pair early detection with zoned response: when something enters the perimeter, that zone's lighting comes on and, where appropriate, the siren marks presence. It is not theatre: behind it is a real detection that has already classified this as a person, so the reaction does not trip on a cat or disturb the neighbours for nothing.
The goal is not to collect videos of attempts, but for there to be no attempt. A perimeter that reacts in time changes the equation for whoever is watching from outside.
Architecture
Why no single layer works alone
Every technology has a blind spot. Radar covers volume but not fine detail; LiDAR measures a plane precisely but not the whole garden; thermal sees heat at night but does not decide what it is; AI decides but needs something to look. Selling any one of them as a complete solution is selling a hole.
The architecture that works in your area combines all four in layers: radar sweeps the open volume, LiDAR seals the critical planes, thermal covers the unlit lines and AI provides the judgement that says whether a detection is a person. The alert only fires when two layers agree, which is why it does not trip on a branch but does on a real intruder.
How many layers and where depends on your plot, not on a boxed package. That is why everything goes through the visit first.
Access points
The weak point isn't the fence — it's the gate
In most homes the intruder does not climb the highest wall; they come in where there is already a gap made for coming in: the vehicle gate, the service door, the gardener's or the pool access. These are the points legitimate people use every day, and precisely for that reason the worst-watched.
In your area we treat those accesses as zones in their own right: detection that tells the expected passage from the one that is not, image verification of who arrives, and a record of every opening. The point is not to turn your home into a bunker, but to know who comes in when you are away, and to make sure an open service door is not an invisible hole.
Where there is domestic staff, maintenance or frequent deliveries, this is the layer that turns a normal flow of people into something traceable, with no friction for you.
Is your system crying wolf?
A technical survey with a photo report: we identify the cause and tell you what is worth keeping.