Vehicle detecting method based on video

Abstract

The invention provides a vehicle detecting method based on a video. The method comprises a daytime detecting mode and a night detecting mode. In the daytime, a method of combination of motion detection and licence plate detection is adopted, in the night, a method of combination of vehicle lamp detection and the licence plate detection is adopted, namely, the licence plate detection result serves as the primary decision criterion of a vehicle, and if no licence plate is detected, in the daytime, the vehicle detected through the motion detection serves as a supplement, and in the night, detected vehicle lamps serve as a supplement, wherein the licence plate detection is carried out based on moving objects, and the detecting range is narrowed. The vehicle detecting method based on the video is high in detecting precision and adaptability, and hardware resources can be saved.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a video-based vehicle detection method. Background technique [0002] The rapid development of my country's urban road traffic has brought great convenience to people's daily life. At the same time, the accompanying negative effects have become increasingly prominent, such as traffic congestion and frequent traffic accidents. To solve such problems, intelligent transportation systems have emerged. The video-based vehicle detection system is an important part of intelligent transportation and a prerequisite for subsequent vehicle behavior analysis and judgment, and its importance is self-evident. The problems of high actual vehicle density, complex lighting conditions, large amount of image data, and real-time performance put forward higher requirements for the design of detection methods. High precision, real-time, high efficiency, and stability have become the basic ch...

AI Technical Summary

Problems solved by technology

[0003] At present, there are many methods for video-based vehicle detection, but these methods have great limitations, such as: 1. The illumination of the actual scene is complex and changeable, resulting in a decrease in detection accuracy; 2. The phenomenon of adhesion is serious, resulting in missed detection and false detection 3. The image resolution is getting higher and higher, and the resulting data volume has increased significantly, resulting in a single frame image processing time is too long, CPU usage and memory usage are high, so it cannot meet the actual needs

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