Vehicle detecting method based on Gauss difference multi-scale edge fusion

Abstract

The invention discloses a vehicle detecting method based on Gauss difference multi-scale edge fusion. The method includes the steps that Gauss scale transformation is performed on images to obtain four Gauss images in adjacent scales; according to the four Gauss images in the adjacent scales, the difference operation is performed between the images in the adjacent scales to obtain three Gauss difference images different in scale, edge detection is performed on the obtained three Gauss difference images through a Sobel operator, then edge fusion with the scale upward searching is performed to remove a lot of background edges while edge information of a vehicle is obtained as much as possible, and expansion, closed operation, hole filling and other series of morphological operation are performed on the fused edge images to obtain a connected domain image representing the vehicle; an outside rectangle of the position where the vehicle is located is determined in the original image according to the position information of a connected domain to detect the vehicle. The images in multiple scales are processed, so that algorithm complexity is reduced, operation amount is reduced, efficiency of vehicle detection is effectively improved, and a good detection result is obtained.

Description

technical field [0001] The invention belongs to the field of video detection, and in particular relates to a vehicle detection method based on Gaussian difference multi-scale edge fusion. Background technique [0002] Moving object detection is a key technology of computer vision and image pattern recognition. Vision-based vehicle detection technology is a research hotspot in intelligent transportation image processing, and has a wide range of applications in the field of intelligent transportation, such as vehicle assisted driving systems, traffic parameter statistics systems, etc. [0003] Vehicle detection methods based on computer vision can be roughly divided into three categories: model-based, neural network-based learning, and feature-based methods. The model-based detection method matches the detected candidate vehicle area with the pre-established vehicle model in the computer database to detect the vehicle, but the disadvantage of this method is that it completely...

AI Technical Summary

Problems solved by technology

The method of detecting vehicles by detecting wheels is easily affected by vehicle driving posture, occlusion and other problems, and the method of detecting vehicle lights is also interfered by street lights and city lights in night scenes, affecting the detection results

The vehicle detection method based on edge detection (including vehicle shadow detection) is inaccurate due to the existence of background edges (such as lane lines, railings, trees, etc.), so how to maximize the detection of vehicle edges while suppressing the background edge , which becomes the key issue to improve the detection accuracy of the method

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