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

A technology of edge fusion and vehicle detection, applied in image analysis, image data processing, instruments, etc., can solve the problems of affecting detection results and inaccurate detection results, achieve good detection results, reduce algorithm complexity, and improve efficiency

Active Publication Date: 2014-07-16
CHANGAN UNIV
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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 edg

Method used

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

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Embodiment

[0049] Collect a 517×363 rainy day traffic image to grayscale to obtain a grayscale image, as shown in figure 1 As shown, select the scale parameter σ=0.5, k=2, and use four Gaussian kernels whose scales are σ, 2×σ, 2×2×σ, and 3×2×σ to perform convolution operations with the grayscale image respectively. Get an unsampled Gaussian blurred image at four adjacent scales.

[0050] The adjacent scale difference operation is performed on the Gaussian blur images of the same size but different scales that have not been downsampled in four adjacent scales to obtain three Gaussian difference images of adjacent scales, that is, the difference between the Gaussian images of scale 1 and scale 2 is obtained. The Gaussian difference map of scale 2 and scale 3 is the Gaussian difference map of scale , and the Gaussian image difference of scale 3 and scale 4 is the difference map of scale . Difference of Gaussian graph as Figure 2-4 shown.

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T7/00
Inventor 赵祥模惠飞穆柯楠杨澜史昕马峻岩
Owner CHANGAN UNIV
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