In a demonstration of “computational periscopy” a US team at Boston University showed they could see details of objects hidden from view by analysing shadows they cast on a nearby wall.
Vivek Goyal, an electrical engineer at the university, said that while the work had clear implications for surveillance he hoped it would lead to robots that could navigate better and boost the safety of driverless cars.
He said: “I’m not especially excited by surveillance, I don’t want to be doing creepy things, but being able to see that there’s a child on the other side of a parked car, or see a little bit around the corner of an intersection could have a significant impact on safety.”
The problem of how to see round corners has occupied modern researchers for at least a decade. And while scientists have made good progress in the field, the equipment used so far has been highly specialised and expensive.
In the latest feat, Goyal and his team used a standard digital camera and a mid-range laptop. “We didn’t use any sophisticated hardware. This is just an ordinary camera and we are all carrying these around in our pockets,” he said.
The researchers, writing in the journal Nature, describe how they pieced together hidden scenes by pointing the digital camera at the vague shadows they cast on a nearby wall. If the wall had been a mirror the task would have been easy, but a matt wall scatters light in all directions, so the reflected image is nothing but a blur. Goyal said: “In essence, computation can turn a matt wall into a mirror.”
They found that when an object blocked part of the hidden scene, their algorithms could use the combination of light and shade at different points on the wall to reconstruct what lay round the corner. In tests, the program pieced together hidden images of video game characters – including details such as their eyes and mouths – along with coloured strips and the letters “BU”.
Given the relative simplicity of the program and equipment, Goyal believes it could be possible for humans to learn the same trick. In a draft blog written for Nature, he said: “It is even conceivable for humans to be able to learn to see around corners with their own eyes; it does not require anything superhuman.”
If you bled when you brushed your teeth this morning, you might want to get that seen to. We may finally have found the long-elusive cause of Alzheimer’s disease: Porphyromonas gingivalis, the key bacteria in chronic gum disease.
That’s bad, as gum disease affects around a third of all people. But the good news is that a drug that blocks the main toxins of P. gingivalis is entering major clinical trials this year, and research published today shows it might stop and even reverse Alzheimer’s. There could even be a vaccine.
Alzheimer’s is one of the biggest mysteries in medicine. As populations have aged, dementia has skyrocketed to become the fifth biggest cause of death worldwide. Alzheimer’s constitutes some 70 per cent of these cases and yet, we don’t know what causes it.
Bacteria in the brain
The disease often involves the accumulation of proteins called amyloid and tau in the brain, and the leading hypothesis has been that the disease arises from defective control of these two proteins.
Bacteria involved in gum disease and other illnesses have been found after death in the brains of people who had Alzheimer’s, but until now, it hasn’t been clear whether these bacteria caused the disease or simply got in via brain damage caused by the condition.
Gum disease link
Multiple research teams have been investigating P. gingivalis, and have so far found that it invades and inflames brain regions affected by Alzheimer’s; that gum infections can worsen symptoms in mice genetically engineered to have Alzheimer’s; and that it can cause Alzheimer’s-like brain inflammation, neural damage, and amyloid plaques in healthy mice.
“When science converges from multiple independent laboratories like this, it is very compelling,” says Casey Lynch of Cortexyme, a pharmaceutical firm in San Francisco, California.
In new study, Cortexyme have now reported finding the toxic enzymes – called gingipains – that P. gingivalis uses to feed on human tissue in 96 per cent of the 54 Alzheimer’s brain samples they looked at, and found the bacteria themselves in all three Alzheimer’s brains whose DNA they examined.
The bacteria and its enzymes were found at higher levels in those who had experienced worse cognitive decline, and had more amyloid and tau accumulations. The team also found the bacteria in the spinal fluid of living people with Alzheimer’s, suggesting that this technique may provide a long-sought after method of diagnosing the disease.
The Victoria Police are the primary law enforcement agency of Victoria, Australia. With over 16,000 vehicles stolen in Victoria this past year — at a cost of about $170 million — the police department is experimenting with a variety of technology-driven solutions to crackdown on car theft. They call this system BlueNet.
To help prevent fraudulent sales of stolen vehicles, there is already a VicRoads web-based service for checking the status of vehicle registrations. The department has also invested in a stationary license plate scanner — a fixed tripod camera which scans passing traffic to automatically identify stolen vehicles.
Don’t ask me why, but one afternoon I had the desire to prototype a vehicle-mounted license plate scanner that would automatically notify you if a vehicle had been stolen or was unregistered. Understanding that these individual components existed, I wondered how difficult it would be to wire them together.
But it was after a bit of googling that I discovered the Victoria Police had recently undergone a trial of a similar device, and the estimated cost of roll out was somewhere in the vicinity of $86,000,000. One astute commenter pointed out that the $86M cost to fit out 220 vehicles comes in at a rather thirsty $390,909 per vehicle.
Surely we can do a bit better than that.
Existing stationary license plate recognition systems
The Success Criteria
Before getting started, I outlined a few key requirements for product design.
Requirement #1: The image processing must be performed locally
Streaming live video to a central processing warehouse seemed the least efficient approach to solving this problem. Besides the whopping bill for data traffic, you’re also introducing network latency into a process which may already be quite slow.
Although a centralized machine learning algorithm is only going to get more accurate over time, I wanted to learn if an local on-device implementation would be “good enough”.
Requirement #2: It must work with low quality images
Since I don’t have a Raspberry Pi camera or USB webcam, so I’ll be using dashcam footage — it’s readily available and an ideal source of sample data. As an added bonus, dashcam video represents the overall quality of footage you’d expect from vehicle mounted cameras.
Requirement #3: It needs to be built using open source technology
Relying upon a proprietary software means you’ll get stung every time you request a change or enhancement — and the stinging will continue for every request made thereafter. Using open source technology is a no-brainer.
My solution
At a high level, my solution takes an image from a dashcam video, pumps it through an open source license plate recognition system installed locally on the device, queries the registration check service, and then returns the results for display.
The data returned to the device installed in the law enforcement vehicle includes the vehicle’s make and model (which it only uses to verify whether the plates have been stolen), the registration status, and any notifications of the vehicle being reported stolen.
If that sounds rather simple, it’s because it really is. For example, the image processing can all be handled by the openalpr library.
This is really all that’s involved to recognize the characters on a license plate:
A Minor Caveat
Public access to the VicRoads APIs is not available, so license plate checks occur via web scraping for this prototype. While generally frowned upon — this is a proof of concept and I’m not slamming anyone’s servers.
Here’s what the dirtiness of my proof-of-concept scraping looks like:
Results
I must say I was pleasantly surprised.
I expected the open source license plate recognition to be pretty rubbish. Additionally, the image recognition algorithms are probably not optimised for Australian license plates.
The solution was able to recognise license plates in a wide field of view.
Annotations added for effect. Number plate identified despite reflections and lens distortion.
Although, the solution would occasionally have issues with particular letters.
Incorrect reading of plate, mistook the M for an H
But … the solution would eventually get them correct.
A few frames later, the M is correctly identified and at a higher confidence rating
As you can see in the above two images, processing the image a couple of frames later jumped from a confidence rating of 87% to a hair over 91%.
I’m confident, pardon the pun, that the accuracy could be improved by increasing the sample rate, and then sorting by the highest confidence rating. Alternatively a threshold could be set that only accepts a confidence of greater than 90% before going on to validate the registration number.
Those are very straight forward code-first fixes, and don’t preclude the training of the license plate recognition software with a local data set.
The $86,000,000 Question
To be fair, I have absolutely no clue what the $86M figure includes — nor can I speak to the accuracy of my open source tool with no localized training vs. the pilot BlueNet system.
I would expect part of that budget includes the replacement of several legacy databases and software applications to support the high frequency, low latency querying of license plates several times per second, per vehicle.
On the other hand, the cost of ~$391k per vehicle seems pretty rich — especially if the BlueNet isn’t particularly accurate and there are no large scale IT projects to decommission or upgrade dependent systems.
Future Applications
While it’s easy to get caught up in the Orwellian nature of an “always on” network of license plate snitchers, there are many positive applications of this technology. Imagine a passive system scanning fellow motorists for an abductors car that automatically alerts authorities and family members to their current location and direction.
Teslas vehicles are already brimming with cameras and sensors with the ability to receive OTA updates — imagine turning these into a fleet of virtual good samaritans. Ubers and Lyft drivers could also be outfitted with these devices to dramatically increase the coverage area.
Using open source technology and existing components, it seems possible to offer a solution that provides a much higher rate of return — for an investment much less than $86M.
De populaire betaal-app Tikkie biedt de mogelijkheid om geld over te boeken naar andere Tikkie-gebruikers op basis van hun 06-nummer. Daardoor was het mogelijk om de IBAN-nummers van vele nietsvermoedende Tikkie-gebruikers te achterhalen, met het gevaar voor identiteitsfraude en phishing.
Dat blijkt uit onderzoek van RTL Nieuws. ABN Amro bevestigt de kwetsbaarheid en heeft de nieuwe functie, Tikkie Pay, tijdelijk offline gehaald. “Bedankt voor de oplettendheid”, aldus de woordvoerder.
IBAN-nummers
Tikkie, dat 4 miljoen gebruikers heeft, toonde met zijn nieuwe functie alle gebruikers uit jouw contactenlijst die hun 06-nummer aan Tikkie hebben gekoppeld. Je kon op een naam drukken, vervolgens een bedrag overmaken en net voor de overboeking de Tikkie annuleren. In de omschrijving van de overboeking zag je dan het IBAN-nummer van de ontvanger, zonder dat diegene daar weet van heeft.
