CarGate

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Cars have a number of unique characteristics that can be used for identification. Automatic number plate recognition is the most commonly employed method to identify a car. Since this method requires the use of a camera, difficult weather and light conditions as well as motion blur adversely affect the recognition rate. Consequently, the CarGate project seeks to use different characteristics such as the sound pattern, magnetic spectrum, and, eventually, even car color to improve the recognition rate.

Sensor Fusion

CarGateThe identification of cars can be vastly improved with reference to two new resources: (1) sound, and (2) magnetic signature.

Most people can easily pick out the sound of a car from other sounds. They can even distinguish roughly the type of car by sound alone. INCAS³ aims to develop a sensor system that can perform a similar task: identify the engine type and even the make of the car based solely on the sound it makes.

The metallic structure of a car creates another possibility for identification using 3D magnetic field sensors. An advanced magnetic field detection system can differentiate between large iron parts such as the motor block and axles. Smart software then assesses the data to see whether the source is a passing car, truck, or motorcycle. The software may even become smart enough to detect vehicle brands and models.

By combining the data from the magnetic field sensors with the data from the sound sensors, it becomes possible to map out traffic streams at an intersection, for example.

Merging these resources with existing license plate photography will result in 100% accuracy in vehicle recognition.

Partner

ParkingwareWithin CarGate, frontier scientific knowledge from the INCAS³ Cognitive Sensor group is combined with the proven industrial expertise from Parkingware. INCAS³ will focus on the development of novel characterization methodologies for sound patterns and magnetic spectrum recognition. Parkingware focuses on improving their license plate recognition system.