Automobile high beam identification system and method based on video deep learning

Abstract

The invention discloses an automobile high beam identification system and method based on video deep learning. The system comprises a foreground part and a background part. The foreground part is used for realizing the identification and treatment of high beam violation behaviors and comprises a road monitoring equipment module, a video processing and identification module, an identification result processing module and a database of violation results to be detected which are orderly connected. The background part is used for processing a video and realizing the deep learning of the video and comprises a key frame extraction algorithm, a database with labels and a deep learning module. The database with the labels is obtained through calling the key frame extraction algorithm to carry out key frame extraction on original video data. The data in the database with the labels is used for training the deep learning module. The trained deep learning module and the key frame extraction algorithm are called by the video processing and identification module. According to the system and the method, a monitoring video is automatically analyzed and identified, the completeness of a law enforcement evidence is ensured, and the system and the method are similar to manual judgment and are intelligent.

Description

technical field [0001] The invention relates to an automobile high beam recognition system, in particular to an automobile high beam recognition system and method based on video deep learning. It belongs to the field of intelligent transportation technology. Background technique [0002] Since the reform and opening up, my country's economy has achieved sustained, stable and rapid development, which has also improved the living standards of our people unprecedentedly, and more and more Chinese people have private vehicles. While the rapid growth of the number of private cars brings convenience to people's travel, it also makes the frequency of traffic accidents higher and higher. [0003] There are many reasons for traffic accidents, many of which are caused by improper use of high beams. At present, the supervision of violations of high beam lights mainly relies on the capture of traffic police. Due to the limitation of police force and time, it is impossible to guarantee...

AI Technical Summary

Problems solved by technology

At present, the supervision of violations of high beam lights mainly relies on the capture of traffic police. Due to the limitation of police force and time, it is impossible to guarantee effective supervision of all violations of high beam lights.

In addition, some high-beam capture systems developed in recent years all recognize the captured pictures, but these methods have certain limitations, as shown in: 1) The number of captured high-beam pictures is small and incoherent. The high-beam pictures are likely to be generated by the driver during normal use, and it is easy to be misjudged as using high-beams indiscriminately. Therefore, using pictures as evidence for law enforcement is not sufficient; 2) In order to obtain these pictures, it is often necessary to set up multiple additional capture devices at the same location , the cost is high; 3) The original video monitoring equipment cannot be fully utilized, resulting in waste of resources

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