How many spaces are open right now?
The cameras are already up. You don’t need a sensor in every space.
We show fifteen cameras here: nine that raise an alert when something’s happening and six that alert nobody, they just count. A thousand-space parking lot already has cameras on the ramps, in the aisles, at the barrier and in the stairwells. IRIS watches them all at once, and out of that come the two things an operator needs: how many spaces are free, and a warning when something goes wrong.
Without IRIS this is a parking lot where nobody knows how many spaces are left.
AI-generated image
What’s at stake in a parking lot
A parking lot lives off occupied spaces and gets judged on the line at the entrance. Both are found out too late. The sign at the door says FULL because somebody set it yesterday, and inside there are forty empty spaces nobody can find: cars circling for four minutes down a full aisle. At the exit a line builds because at one pay machine nobody’s finishing, and nobody notices until the jam reaches the street.
IRIS watches for you. It analyzes all the cameras the parking lot already has — the ramps, the aisles, the barrier, the charging spaces, the exits — and only interrupts when a rule you wrote is met: a car coming up the ramp the wrong way, a vehicle stopped outside a space on a level that should be empty, smoke at the foot of a plugged-in car at three in the morning. The alert arrives with the camera, the time and the video, while it’s happening, to the person who can walk down to that level.
And where there’s nothing to alert about, IRIS measures. How many spaces are free, on which level and of which type. How many cars are waiting at the entrance and for how long. How long a car takes from coming up the ramp to parking. That isn’t set up by talking, it’s set up by drawing: a line can’t be described in a sentence, it has to be traced onto the image. That’s why the figure comes out the same every day and can be audited. And it comes from the cameras that are already up: no sensor over every space, no digging up the tarmac, no rewiring.
The real scale
Fifteen cameras here. A thousand spaces and one person in the booth.
This demo shows fifteen cameras because fifteen fit on a screen. A thousand-space parking lot has many more, and has had them since the day it opened: on every ramp, in every aisle, at the barrier, in the stairwells and in the lobby. What it doesn’t have is anyone watching them. There’s one person in the booth, and that person is taking payments, raising barriers and answering the intercom; they can’t also keep an eye on eight levels. More screens don’t fix it either. IRIS analyzes however many cameras there are, day and night, and only interrupts when a rule you wrote is met. The rest of the time it counts: free spaces, cars in the line, minutes to park.
Examples
These fifteen are just examples.
Nine raise an alert when something’s happening and six alert nobody: they just count. These aren’t the fifteen things IRIS can do in a parking lot; they’re the ones that fit on a screen, and each operator writes them in their own words. Open any of them and you’ll see the image as it is, and then with what IRIS understands on top.
See it for yourself
We won’t just tell you. We’ll show you.
It’s a summary of the real screen: it plays by itself, step by step, and shows only the path of one case. The actual tool has far more than fits in here.
- Checks the whole system
- Flags what is failing
- Puts it on one screen
Now think about your own parking lot
Imagine knowing it while it’s happening, and not when somebody complains at the desk.
The forty free spaces on level four that nobody knows are free. The car that has spent four minutes circling a full aisle. The sign at the entrance saying FULL when it isn’t. The six cars waiting at the barrier and the line already spilling into the street. The exit machine where nobody’s finishing. The two cars that crossed the barrier on a single opening. The one that’s been three hours in a thirty-minute space. The one that’s been in the same spot for days and nobody remembers when it arrived. The car parked in the reserved space, with somebody waiting in front of it. The one that finished charging an hour ago and is still on the charger. The one coming up the ramp the wrong way. The one that reaches the crossing just as somebody’s on it. The door flung wide open against the car in the next space. The vehicle stopped outside a space on a level that should be empty. The smoke at the foot of a plugged-in car at three in the morning. And the emergency exit blocked by a car.
All of that is happening in front of cameras the parking lot already has, and today nobody knows until somebody complains or until someone sits down to review the recording. IRIS watches all those cameras at once and says it while it’s happening, to the person who can walk down there. No sensor in every space, no digging up the tarmac, no camera replaced.
What gets asked before signing
The ten questions that come up around the parking lot table.
Answered in the first line, straight out. Including what doesn’t read well on a backlit ramp, because that’s something to know before, not after.
Do I need a sensor in every space to know how many spaces are free?
No, you don’t need a sensor in every space. The figure comes from looking at the spaces with the cameras the parking lot already has: you draw on the image which patch of floor each space is, and IRIS looks at whether there’s a car on it or not. One aisle camera covers dozens of spaces at once, and it also tells the type apart: reserved, accessible, motorcycle or charging. No digging up the asphalt, no cabling floor by floor, and nothing that already works has to be moved.
Does it work with the cameras I already have, with the light there is down there?
It does work with the cameras you already have, and it’s worth saying where it falls short. The mouth of a backlit ramp burns out the image at midday. A floor with the bare minimum of lighting leaves aisles where a plate can’t be read. A corner dome fitted to watch the door won’t tell the far spaces apart. During the project we check camera by camera what can genuinely be detected in that frame, and we tell you plainly which ones fall short, which would only need tilting a few degrees, and which are best left for plain watching. Replacing a camera is the exception, not the starting point.
Is the figure on the entrance sign reliable? And when the parking lot is full?
The number on the sign is reliable because it doesn’t come from subtracting entries and exits: it comes from looking at the spaces. Counting cars at the barrier piles up the whole day’s error, which is why by mid-afternoon the sign says “full” with forty free spaces on level four. Here every space is checked one by one, and the number corrects itself. Now the awkward part: if a column hides half a space, or an aisle has no light, that particular space reads worse. That shows up during commissioning, space by space, and you decide which camera moves and which area stays out of the count.
Does it read license plates? And is that not personal data?
Yes, IRIS reads license plates, and yes, a plate is personal data. We say it plainly because that’s what the law says and because it’s worth knowing before signing. That’s why reading is your decision, camera by camera: you can switch it on at the barrier for season tickets and leave it off in the aisles. And you configure what is kept, for how long and who can look it up. What IRIS doesn’t do is tell you whose car that is: it queries no vehicle register and no database of people. Matching a plate to an owner is a matter for your season-ticket system and your organization, not for us.
Does IRIS identify people or drivers?
No, IRIS does not identify people or drivers. It does not recognize faces, does not compare them against any database and does not store who anyone is. What it sees is “a person crossing the walkway,” “a car going up the ramp,” where it’s and since when. In the blocked fire-exit alert, what arrives is that there is someone on the floor and a car in front of the door: never who that person is. And counting is aggregated and anonymous: it says how many cars and how many people went through an area and at what time, never who they were. A counted person is a one, and nothing else remains of them.
How many alerts are we going to get in a day?
You will get the alerts your rule asks for, and tuning that rule is the work of the first few weeks. A system that fires two hundred alerts a day gets ignored within four, and in a parking lot that happens fast because there’s movement all the time. That’s why a rule is never “alert if a car is stopped.” It is “alert if a car has been stopped outside a space on level four for more than a few minutes, during opening hours.” You bound the area on the image, the hours, how long it has to last and who receives it. Then you review a week of alerts with your team and adjust. An alert nobody attends to is a badly configured alert, and that gets fixed.
Does the video leave the parking lot or stay inside?
The video leaves the parking lot or stays inside depending on the mode you choose, and there are two modes. In local mode it doesn’t leave: the server sits in your own building, the video comes in over your network, it’s analyzed there and the system doesn’t need internet to work. That’s the usual choice in an underground garage, where coverage is poor anyway. In the other one it is processed in our data center — which is our own cloud, not a third party’s — and then it does leave, encrypted and under the terms set in the contract. With several parking lots it is worth deciding early, because one server per building isn’t the same as bringing everything to a single place.
Does it replace the barriers, the ticketing system or the fire protection system?
No, it doesn’t replace the barriers, the ticketing system or the fire protection system: it lives alongside all three. IRIS opens and closes nothing, takes no payment, validates no season ticket and cuts no charger’s power. What it does is see what is happening and alert whoever can go down there. And here’s the important part, said plainly: when it sees smoke or flame at the foot of a plugged-in car, it raises an alert, but it isn’t an approved fire detector and it doesn’t replace the detection and suppression system your building already has to have. It’s an extra pair of eyes, before the ceiling detector goes off. Never instead of it.
What does IRIS NOT know about a parking lot?
IRIS doesn’t know plenty of things about a parking lot, and they’re better said early than late. It doesn’t know whether someone has paid: that lives in your ticketing system, not in the image. It does not know whose car that is. It doesn’t know whether the person parked in the reserved space is entitled to be there — it only sees that a car is there and that no authorization is on record. It doesn’t know what is inside a closed vehicle. And it sees nothing that isn’t in a frame: if there’s no camera on that corner, there’s no data there. What it does know is what happens in front of the cameras you already have, and today nobody knows that until somebody complains.
How much does it cost to put IRIS on a parking lot’s cameras?
You pay per camera. What it comes to depends on how many cameras are analyzed, what’s detected or measured on each, how many parking lots are included, which deployment mode you choose and whether the hardware is yours or ours. We don’t publish a single price list because it wouldn’t be honest: measuring the spaces on one level doesn’t cost the same as covering six levels, two ramps and the barrier. What you can do is start small: one level, two or three rules and open-space counting, and look at the alerts and the figures with your team before deciding anything more. Tell us how many spaces and how many cameras you have and we’ll give you a figure. Write to hola@infinityneural.com. Or call Spain: 011 34 902 02 70 91.
Next step
Who sees the sales that never reach the register?
10 cases: 4 raise an alert when something happens and 6 only measure. You see each image as it is, and then with what IRIS understands on top.
Who is watching when the load swings over somebody?
11 cases: 9 raise an alert when something happens and 2 only measure. You see each image as it is, and then with what IRIS understands on top.
And the best part
What happens outside the framedoesn’t exist for IRIS. No camera sees the whole world.
So the first thing we do is check what each camera covers and write it down: from here you can understand this, and not that. A dull hour that saves a lot of misunderstandings.
Tell us your problem and we’ll say whether IRIS understands it, or not yet.
A person answers, the same working day.