(Sorry this one got very long…I surprised myself…)
@stephenbaileymagic In the physical security world, I think your approach makes a lot of sense. I see two applications of TinyML. The first are the alarm sensors themselves.
For example, passive Infrared (PIR) detectors are very common because of their low cost and longevity. Unlike door/window sensors (which really can be binary), PIRs use a small array and simple rules to attempt to differentiate a human from moving another heat source. They are very low tech.
For example, “pet immune” or “pet-proof” sensors have specific mounting requirements (minimum height above the floor, specified straight-line distance from any place the animal can be, not facing stairs, etc. Then they basically use movement, size, and vertical detection to differentiate between a cat or dog and an intruder.
If installed perfectly, they’re reasonably reliable. But, for example, they can’t point toward ascending stairs, or a 10 lbs cat walking down the stairs looks like a person to the sensor. Similarly, if a cat jumps off an object, off the side of the stairs, etc., as cats love to do, they look very tall and trigger the sensor.
For dogs, PIRs rely mostly on the size of the heat signature. That’s why you’ll see weight limits (e.g. pet proof to 100lbs). They’re essentially betting that an 80-pound criminal isn’t going to crawl like a dog.
PIRs also require a minimum movement speed, generally sideways across the sensor. That helps eliminate false alarms when the furnace kicks in on a cold day, but for example, installers have to be careful that a floor register isn’t going to heat a heavy curtain and move it around. If you want to have some fun, put an alarm on test and walk very slowly in a straight line toward the sensor.
Even when properly installed, PIR false alarms are too frequent. With the rise of DIY alarm installations, things get even worse because people install PIRs without understanding how they work.
Of course in an industrial setting, there are bigger problems. A mouse that runs right in front of a sensor looks massive, and even a large bug crawling across the sensor of some devices can create a false alarm. So, there are so-called dual-tech devices that combine PIR with microwave detection and require two the different detection systems to fire at the same time. However, microwave is much more expensive, active, contributes to RF noise, and can’t run for long on batteries (as compared to passive PIR detectors that can run on a battery for a few years.) Microwave is also not without false alarm issues. A print job on a laser printer in the middle of the night has been known to do the trick.
Acoustic glass break sensors are a good option to deal with areas where motion sensors may be problematic, or as a second intrusion indication (more on that later). They can also cover a much larger area and are sometimes more cost effective and aesthetically pleasing than putting contact sensors on multiple windows. Glass breaks, as they are called in the field, have become more sophisticated, but could be improved. The better ones detect the sound of an impact followed by the specific frequencies emitted by framed glass breaking. In theory, you can use them 24x7 (i.e. no armed/disarmed state needed). However, in practice I’ve seen them triggered by dropping a toilet seat in a power room, putting down a stack of dishes too hard, and most famously by a cat knocking a large plastic bin of dry cat food off the counter onto the floor.
So my first thought is to improve the sensors themselves. Basic wired systems these days still use a closed loop with an end-of-line resistor, and wireless sensors basically send a serial number plus a few bits for different conditions (motion, low battery, etc.). Most wireless alarm sensors in use today have little if any security on the RF side. For example, you can use a cheap SDR setup to monitor all but the latest Honeywell systems, and with a bit of work you can impersonate any of the devices. But with some people looking to ZigBee, etc., in the future more intelligent devices could send more information. Even an integer with a confidence of 0-255 would be an improvement.
The second area is the alarm panel itself. Both installers and alarm monitoring centers are essentially forced to use tradecraft to overcome the limits of binary alarm sensors and the dumb alarm panels that report them.
For example, in a house with a pet, it is unlikely that you’d install a PIR in each room. Take a look around your livingroom, for example, and see if you can find a PIR location that doesn’t face the street (car headlights), doesn’t face a heat register, and where a cat can’t come within 6 feet of the sensor (straight line measurement from sensor in any direction.) Easy if there is no furniture.
I have a love-hate relationship with PIRs. We them to detect movement, otherwise the system is incomplete. We can’t have a situation where an intruder can pry or break in through a window and not be detected. But every PIR installed increases the false alarm potential.
Similarly, unless the house is pre-wired or the basement ceiling is open, it’s difficult to install contact switches (mechanical or magnetic) on a lot of newer windows. Even if the basement ceiling is open, drilling through the window frame into an exterior wall and trying to shove a wire through is nasty business.
So a common solution is to put a contact sensor on each exterior door, install a PIR in a hallway (through which you hope the intruder is forced to pass), perhaps use a glass break detector to cover the most vulnerable areas, and then the lesser known installer trick of wiring internal doors and configuring the alarm panel to believe that the internal door contacts are motion sensors so that they apply the same armed away/armed stay/disarmed logic.
Why? The dirty little secret of alarm monitoring is that they want to be able to ignore (or at least not dispatch the police) when (not if, but when) the PIR triggers a false alarm.
Instead of making a more intelligent alarm panel that understands, for example, that in the absence of any other devices triggering, a single hit from a PIR is almost always a false alarm, the mainstream alarm industry has to work around that limitation.
Unfortunately the person in the monitoring center trying to make that decision doesn’t know anything about the layout of the building. They might have a list of sensors, but they generally don’t have the time or information to follow the logic, “Ok, the front door hasn’t been opened, and the glassbreak covering the back of the house hasn’t been triggered, but the PIR in the hallway just inside the front door detected motion once” vs “hallway PIR tripped multiple times plus bedroom door opened”. In a lot of cases the alarm monitoring center doesn’t want the liability, so in the absence of specific directions they will call a keyholder. They might get someone like me who will say, “One hit on one PIR? Thanks for the call, don’t dispatch, I’m going back to sleep.” or they might get someone who is terrified that their home is being burgled while they are out of town, in which case now the police are going to end up doing a perimeter check and leave them a ticket or send an invoice.
There is such a high false alarm percentage that police generally de-prioritize alarm calls. The public see ads of digital walls protecting them and near instant police response. Reality is very different, most forces won’t accept an alarm call over 911. Some forces will not respond until the alarm monitoring center contacts a keyholder and gets instructions. However, some will prioritize a call that meets certain verification procedures, and in limited cases (for example with video verification) they might even accept a 911 call and respond on a confirmed crime-in-progress basis.
Even worse, some will stop responding at all after a certain number of false alarms. It is not difficult for a malicious person to intentionally create false alarms, but I won’t discuss those techniques here.
There are alarm panel features to help with false alarms. For example, if you disarm the alarm within a certain period of time after alarm activation, the panel can send a “cancel” signal.
Some have a feature called “cross zone” – the concept is that more than one specified zone has to trip before the alarm is activated. But, presumably being risk adverse, some panels will enter a “trouble” state when only one of the configured zones is tripped. While that is an improvement in terms of false alarm reduction, it usually means that authorized people are greeted by a loudly beeping alarm panel warning of “trouble” that they have to manually reset when, in fact, all that has happened is the cross zoning feature prevented a false alarm. Not a winner.
Working with most alarm panels, especially the mainstream ones, is like a trip back to 1980. They’ve added wireless, WiFi, and cellular, but the alarm detection logic is essentially unchanged in decades.
From an ML perspective, a false alarm and a real intruder result in different patterns when sufficient alarm sensors are present. I think there’s an opportunity there, especially if combined with smarter sensors.
Thanks!