Weighted local feature comparison based vehicle fake plate identification method and apparatus

Abstract

The invention discloses a weighted local feature comparison based vehicle fake plate identification method and apparatus. The method comprises: based on a snapshot picture of a to-be-identified vehicle, identifying vehicle type information and a license plate of the to-be-identified vehicle, and extracting a local feature graph in the snapshot picture; then, extracting reference local feature graphs corresponding to vehicles with the same license plates in a reference comparison library, performing comparison one by one, and calculating the similarity between each local feature graph and the reference local feature graphs; and at last, according to a set local feature weight corresponding to the to-be-identified vehicle, calculating the total similarity between the to-be-identified vehicle and the vehicles with the same license plates in the reference comparison library by summarization, and according to the total similarity of the to-be-identified vehicle, judging whether the to-be-identified vehicle is a vehicle with a fake plate or not. The apparatus comprises a picture identification module, a local feature graph extraction module, a license plate search module, a comparison module, a total similarity calculation module and a pre-warning module. According to the method and the apparatus, the judgment and identification of suspected vehicles with fake plates are accelerated and the identification is more accurate.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic management, and in particular relates to a vehicle deck identification method and device based on weighted local feature comparison. Background technique [0002] With the development of my country's automobile industry and the improvement of people's living standards, automobiles have entered the family and become a necessity of life. However, the appearance of counter-licensed vehicles has greatly damaged the interests of license-plate owners, and has added unrest to the society. Therefore, in the field of intelligent traffic management, cracking down on counter-licensed vehicles has become an important task in law enforcement. [0003] The typical feature of the set car is that the license plate is the same, and the model is roughly the same, but there must be differences in local features, such as car stickers, car decorations and other details. In the prior art solution, the iden...

AI Technical Summary

Problems solved by technology

The workload of this method is very large. Due to the large number of vehicles, it is almost impossible to traverse all the passing data. Generally, only when dealing with illegal vehicle records, it is compared with the vehicle information registered by the vehicle management office. From this, it is found that some Set-up cars cannot effectively curb set-up cars

[0004] In addition, when comparing existing technical solutions, the comparison is often based on the entire snapshot of the vehicle, which is prone to interference from people in the cab area and vehicle lights, and the comparison area is too large and the amount of calculation is too large

At the same time, because the set of license plates may only have differences in subtle places, but most of the areas are the same, when the comparison area is too large, the influence of the subtle areas on the whole will not be so prominent, and it is easy to fail to compare

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