CAM-932
Blocky
A shopping street at midday, seen from above. There is a red café awning on the left, people walking along the pavement, a group of three on the right, cars parked at an angle and a scooter down at the bottom. And all of it has fallen apart into blocks: the roof of the white car in the middle is a flat smear, the scooter is grey mush, faces are not faces and the number plates have no letters in them. You can see that a car goes past; you cannot see which one. IRIS sees that the detail from this camera has dropped far below what the same view gives when it is healthy. What it does not know is whether the bottleneck is the camera or the path.
Turn on IRIS Vision
AI-generated image
What IRIS understands
What it recognises in this scene
- blocks instead of detail
The rule
You decide once. IRIS applies it always.
If
the picture breaks into blocks and the detail drops below what that camera needs for its job
Then
- alert maintenance with the camera, the time and the blocky video
- put the healthy image of the same view alongside, to show how much has been lost
- note that while it lasts, that camera is not fit for reading detail
The instruction that set it up«Tell me when a camera stops giving the detail I need to read what is in the picture.»
What changes
What changes for whoever answers for the cameras working
A scrape between a scooter and a car in this street gets sorted out with the video: who was where, and with what plate. The camera was mounted, it was on and it recorded. And when the file is opened, the car is a white rectangle. That detail was not lost in the viewing: it was lost in the saving, and from there it does not come back. The expensive part is not the fault, it is finding out late. With IRIS the alert goes out the day the detail drops, with the healthy image from that same camera alongside for comparison. Often it is sorted without going up the pole, by changing how the stream is sent; sometimes it is not. But you know, and you know in time.
What people usually ask
Is blocky footage any use as evidence?
It is useful for some things and not for others, and it is better to know that in advance. It shows that something happened, at what time and where. It does not let you read a number plate or tell people apart at middle distance, because that detail is not in the file: it was not lost in the viewing, it was lost in the saving, and there is no way to bring it back. Whether a video is accepted is decided by whoever handles the case, not by us. Which is why it matters to find out the day it starts, not the day it is asked for.
Compression is normal. Where is the line?
The line is set by what each camera is there for. A context camera, which only has to say whether there are people in the square, can take a lot of compression and still do its job. One placed to read plates at an entrance can take almost none. That is why the threshold is set camera by camera, and it is written by the site operator, not by us. IRIS raises the alert when that camera crosses its own threshold, not a general number that would be wrong for half the estate.
Is it the camera or the network?
It can be either, and sometimes both at once. IRIS does not diagnose it and will not pretend to: it says which camera, since when, how far the detail has dropped and with what picture. From there the trail is simple. If it happens to one camera, look at that camera. If it happens to every camera on the same run at the same hour, look at the path. It is the fact the search starts from, and today it does not exist until somebody complains that nothing can be read.
The real interface, step by step
You set the rule once. IRIS applies it every time.
You will see a summary of the interface, played step by step and hands-free. Each round starts with another case. The complete tool does not fit in a demo.
- The hour, as a grid
- You look where it's dark
- You jump to that minute
Next step
How long before somebody comes down that corridor at four in the morning?
13 cases: 12 raise an alert when something happens and one only measures. You see each image as it is, and then with what IRIS understands on top.
Who sees the child who has fallen at the far end of the playground?
13 cases: 12 raise an alert when something happens and one only measures. You see each image as it is, and then with what IRIS understands on top.
And the best part
There are nights of thick fogwhen the image gives you nothing. We do not dress that up.
Backlight, heavy rain, a street lamp that has blown. IRIS works with what the camera puts in front of it; when there is no image, it says so instead of inventing an answer.
Tell us your problem and we will say whether IRIS understands it, or not yet.
We reply the same working day.