Vehicle traffic behavior detection method, device, equipment and storage medium

Abstract

The embodiment of the invention discloses a vehicle traffic behavior detection method, device, equipment and storage medium. The method includes: compressing and splicing the sequentially acquired multi-frame initial vehicle images, and detecting and analyzing the compressed and spliced ​​target vehicle images to obtain vehicle attribute information in each frame of the initial vehicle images. By adopting the above solution provided by the present invention, it is only necessary to perform image detection and analysis once on the target vehicle image composed of multiple frames of initial vehicle images, and the vehicle attribute information of each motor vehicle contained in the multiple frames of initial vehicle images can be obtained at one time. , thereby shortening the detection time spent in obtaining the vehicle attribute information in the single-frame initial vehicle image, and improving the detection efficiency of vehicle traffic behavior.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of monitoring equipment, and in particular, to a vehicle traffic behavior detection method, device, device, and storage medium. Background technique [0002] With the increase of road traffic flow year by year, road traffic problems have become more and more serious, and the management of road traffic and the supervision of violations have become more important. For example, road traffic violations such as motor vehicles not paying attention to avoiding pedestrians or running red lights at intersections without traffic lights are relatively frequent. [0003] At present, an image collector is usually used to capture the vehicle driving image of the motor vehicle at the intersection, and image analysis is performed on the vehicle driving image to detect whether the motor vehicle avoids pedestrians at the intersection or whether the motor vehicle runs a red light. However, in the actual ...

AI Technical Summary

Problems solved by technology

However, in the actual application scenario, if the speed of the motor vehicle near the intersection is too fast, there will be a problem of the motor vehicle rushing forward. For example, when the previous frame image is captured, the motor vehicle has not entered the illegal judgment area. , the next frame of image may show that the vehicle has crossed the illegal judgment area, so that it is impossible to capture the effective illegal evidence map that the motor vehicle did not avoid pedestrians to cross the intersection or crossed the intersection by running a red light, resulting in the inability to effectively track illegal motor vehicles

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