author: Michael Cheng

License plate scanning has become an incredibly popular method of identification for law enforcement groups, tollbooth stations, intersections and automated checkpoints on public roads. The technique is widely used, due to its ability to accurately detect alphanumeric characters with very limited human intervention.

In London, 10 speed cameras installed over busy roads bring in a whopping $3.89 million worth of fines and tickets annually.

Over the years, drivers have developed a plethora of sneaky tactics to disrupt license plate scanning cameras around cities. Some add a dark film over their plates, while others go as far as installing infrared LED lamps around the borders (this reduces the effectiveness of cameras that utilize non-visible infrared beams for detection during nighttime conditions).

New Solutions

In a move to maintain order on busy roadways, Chinese researchers have uncovered a new technique that identifies cars based on unique characteristics. Unlike traditional license plate scanning, this method does not focus on the vehicle's license plate characters. Instead, it leverages robust cameras that identify cars using scratches, inconsistent markings and other uncommon features.

According to Peking University researchers, the group responsible for the AI-powered system, the new technique is capable of catching criminals who frequently change their license plates in order to mislead cameras. Moreover, it can reduce detection errors during periods of low visibility, such as during fog, rain or snow. Without needing to decipher alphanumeric characters, systems would never be in a position to confuse the number ‘8' with the letter ‘B' or the letter ‘O' with the number ‘0'.

"We want the deep network to generate two independent sub-features from two different levels," wrote the scientists. "Each sub-feature can embed more discriminative information for that level and can be better used to perform precise retrieval tasks."

Repression Network

Interestingly, the identification method can also be used to track the faces of humans. The AI-powered network that underpins the system continuously scans images fed into the network by the cameras. It keeps track and "remembers" the images, so that when it "sees" the object again, it will simply recall the classification. According to researchers, this method is effective in detecting vehicles moving at extremely fast speeds.

"Precise vehicle search, aiming at finding out all instances for a given query vehicle image, is a challenging task as different vehicles will look very similar to each other if they share same visual attributes," said the Peking University researchers. "The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from large-scale image databases."

At the moment, the cutting-edge platform is still being tested and developed. It is not being utilized or trialed on public roads at this time. A major hurdle in deploying the identification method is the generation of false results. A car with leaves and debris could be improperly identified, when solely using this technique.

During the early stages of deployment, it is likely that the repression network will be used as a secondary form of identification, should license plate scanning fail or generate erroneous results.