A vehicle feature extraction and matching method based on video

Abstract

The invention discloses a vehicle feature extraction and matching method based on video, comprising the following steps: reading a video file; performing video image background modeling; the background subtraction method is used to obtain the foreground region containing moving objects. Vehicle detection is carried out by acquiring vehicle contour. Extracting pHash feature from vehicle image; extracting vehicle image ISIFT features; combined with pHash algorithm and ISIFT algorithm, and through BBF search algorithm and RANSAC algorithm to screen ISIFT feature matching points, so as to achievevehicle matching. The invention can realize the functions of vehicle detection, vehicle characteristic information extraction and vehicle matching based on the video, and can detect the vehicle outline of the vehicle very accurately, extract the vehicle information characteristic accurately, meet the real-time requirement with high operation speed and low cost, and can detect the vehicles traveling in different directions, and has wide application range.

Description

technical field [0001] The invention belongs to the field of image processing and traffic video detection, and relates to a video-based vehicle feature extraction and matching method, which is mainly applied to video-based vehicle detection, vehicle feature information extraction, and vehicle matching. Background technique [0002] The cost of extracting traffic information through video is low, and only need to use the capture camera at the traffic checkpoint. Extracting various information of vehicles involves computer vision and image processing technology. With the continuous enhancement of computer computing power, it is possible to accurately extract vehicle information through digital video. However, this method still has many difficulties. For example, the bayonet camera is installed outdoors, which is greatly affected by factors such as light and environment. In addition, due to the complex and changeable traffic scene, it is difficult to guarantee the accuracy of s...

AI Technical Summary

Problems solved by technology

However, this method still has many difficulties. For example, the bayonet camera is installed outdoors, which is greatly affected by factors such as light and environment. In addition, due to the complex and changeable traffic scene, it is difficult to guarantee the accuracy of s when vehicles are dense or in emergencies. Robustness and accuracy; and real-time issues: the latest algorithms can often have good accuracy, but the amount of calculation is huge, very time-consuming, and difficult to be practical

Existing retrieval methods generally need to collect samples, and also need to take a pure background image without moving objects in the image, which requires a lot of preparatory work

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