An Automated Number Plate Recognition (ANPR) system is a computer-vision-based technology advancement that has revolutionized ‘on the road’ monitoring and security solutions – from 24/7 parking lot management to law enforcement. For instance, the US Department of Justice says that “93% of police departments in cities with populations of 1 million or more use their own ANPR systems, some of which can scan nearly 2,000 license plates per minute.”
It goes to show the valuable role of ANPR in ensuring the security of citizens while streamlining the real-time flow of transport. Let’s look at some of the use cases driven by ANPR technology:
Basically, ANPR helps identify letters, symbols, and colors of vehicle registration plates in order to collect valuable insights such as vehicle number, location, travel patterns, etc. Requiring zero human intervention, its sensor looks to capture images of vehicles on the road – from fast-moving vehicles that break violations to slow-moving ones that enter and exit parking lots. Hence, choosing the right image sensor for number plate recognition must be done meticulously.
Let’s look at some of the important factors to be considered while choosing a sensor for ANPR.
Three factors to consider while choosing the image sensor for ANPR
Motion blur and rolling shutter effect are two of the major challenges that number plate recognition applications have to overcome. Otherwise, it would be impossible to capture the number plate characters of the vehicle in motion, especially in the case of fast-moving vehicles. Other challenges include varying lighting conditions, headlight glares, and harsh environmental conditions.
If you want to stay on top of these challenges while selecting a sensor, you should consider these three crucial factors.
There’s no debating that the cameras used in ANPR must capture images without any distortion or skew. If you use a rolling shutter sensor, that will produce artifacts in the output image, making the characters in the number plate virtually unrecognizable. However, with a global shutter, the camera exposes the sensor simultaneously, enabling it to capture a fast-moving image in its entirety – that too, without any artifacts. It means that you will be able to produce images of moving vehicles without any distortion.
As you may already know, the camera exposure sets the parameters on how sharp or blurry a captured image can be. When it comes to ANPR systems, the exposure time should be dictated by how much light tends to illuminate the vehicle’s number plate as well as the speed of the vehicle. A well-lit number plate can be captured with a low exposure time. This also helps to ensure that a frame is captured fast enough to reduce any motion blur that could potentially occur due to the vehicle’s speed. Also, given that the environment in which these cameras are installed is subject to unpredictable lighting conditions, the sensors should be equipped to adapt to varying exposure conditions.
Another critical factor in effectively capturing number plate information is to ensure that a heavy headlight does not bleach the image. It would hinder Optical Character Recognition (OCR) – making the vehicle’s number plate a lot less recognizable. A sensor with a good dynamic range can go a long way to capture the information on the plate without overexposure due to the surrounding headlight.
Take a look at this comparison to understand the difference between the image output of a normal camera and an HDR camera.
Automated Number Plate Recognition (ANPR) systems have been powerful catalysts in the global race towards improving the management and safety of vehicle users. With more smart cities emerging all over the world, the need to make roads safer and more streamlined, especially in urban areas, has skyrocketed over the past few years. Whether monitoring hundreds of parking slots, clearing traffic congestions, or instantly identifying traffic violators, the effectiveness of an ANPR system depends on the type of integrated camera. It’s why selecting the right camera for this application must be based on critical factors like shutter artifacts, exposure times, and dynamic range to cater to surrounding light conditions.
ANPR cameras from e-con Systems: