Method and system for detecting and tracking vehicle based on machine vision technology

Abstract

The invention discloses a method for detecting and tracking a vehicle based on the machine vision technology. The method comprises the step of detecting a structuralized road, wherein a picture is preprocessed, the edges of the picture are enhanced and are mediated in a linear mode, the edges of the road are extracted, and the road region is determined; the step of detecting the moving vehicle, wherein the vehicle in the determined road region is initially recognized, the region of interest is further diminished, and whether a vehicle to be detected exists in the determined road region or not is recognized for the second time; the step of tracking the moving vehicle, wherein the accurate position of the target vehicle is determined through repeated iteration tracking based on the mean value drifting algorithm. The invention further discloses a system for detecting and tracking the vehicle based on the machine vision technology. The vehicle can be distinguished more effectively based on the maximum variance threshold value extraction method, the vehicle can be detected more accurately based on the fusion character algorithm, the region of interest is determined, the secondary shrunk ROI region is detected through an adaboost classifier, the operand is greatly reduced, the real-time performance is improved, and meanwhile the accuracy that the system detects the vehicle is improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a vehicle detection and tracking method and system based on machine vision technology. Background technique [0002] When we study intelligent vehicles, the core issue is how to enable them to perceive the surrounding environment, extract information, distinguish obstacles ahead, and use this information to complete navigation tasks. Collect information through visual navigation, so it has more obvious advantages than other sensors in terms of information volume and collection speed. On the one hand, visual navigation can not only collect the position of the target in the surrounding environment, but also include the external outline, color features, and texture of the target. Features, edge features, etc., these are unmatched by other sensors; on the other hand, using visual navigation can well detect the edge of the road, as well as some artificial warning signs, road signs and ...

AI Technical Summary

Problems solved by technology

Using a single method is bound to fail to effectively detect vehicles. With the improvement of computer hardware and software, how to effectively integrate multiple methods, multi-vehicle detection is an inevitable trend

Images