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Aerial video highway lane line detection method based on line spacing characteristic point clustering

A lane line detection and feature point technology, which is applied in the field of image processing and traffic video detection, can solve the problems of low detection speed, low accuracy, and inability to meet the needs of real-time detection

Active Publication Date: 2019-03-01
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

However, the existing detection methods are either inaccurate or the detection speed is low, which cannot meet the needs of real-time detection.

Method used

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  • Aerial video highway lane line detection method based on line spacing characteristic point clustering
  • Aerial video highway lane line detection method based on line spacing characteristic point clustering
  • Aerial video highway lane line detection method based on line spacing characteristic point clustering

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Embodiment Construction

[0097] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0098] The present invention provides a method for detecting highway lane lines in aerial photography video based on line spacing feature point clustering, the process of which is as follows figure 1 shown, including the following steps:

[0099] Step 1: Read Video Frames

[0100] Read the video file from the drone's on-board camera to obtain a frame of color image F with the size of W×H×3, where W and H are positive integers, representing the width and height of the color image, respectively.

[0101] Step 2: image segmentation processing, including the following sub-steps:

[0102] Step 2.1: Downsampling

[0103] Let the sampling ratio be s x ,s y , then t...

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Abstract

The invention discloses an aerial video highway lane line detection method based on line spacing characteristic point clustering, which comprises the following steps: reading video frame; Image segmentation; equilibrium from a large number of data points, fully extract the characteristic points of each lane line; the similarity matrix is constructed based on the similarity measure method of line spacing, and the characteristic points of different lane lines are clustered. The cubic B-spline model of lane is established and the parameters of the model are estimated by the improved RANSAC algorithm; correction and prediction of lane parameters. The invention detects and pretreats the edge of the expressway in the video of the aerial shooting of the expressway, Reducing the processing time ofthe next frame, effectively removing the interference pixels outside the freeway, has a better clustering effect on lane feature points, can get more accurate and more stable lane fitting effect, andcan achieve real-time processing effect.

Description

technical field [0001] The invention belongs to the technical field of image processing and traffic video detection, and relates to a lane line detection method, which is mainly used in aerial highway video. Background technique [0002] In recent years, the UAV-based highway violation detection method has been widely proposed. Because the UAV is located at a high position, the monitoring range is wide, and the moving camera can track and detect more vehicle violations, it can control the highway more effectively. Vehicle violations on the road. At present, the detection of illegal behavior of vehicles on the road, such as illegal occupancy of emergency lanes and driving in violation of the prescribed lanes, is based on the accurate detection of lane lines and road edges. Man-machine detection of highway vehicle violations is of great significance. However, the existing detection methods are either not accurate enough, or the detection speed is low, which cannot meet the n...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/46G06K9/62
CPCG06V20/182G06V20/588G06V10/48G06V10/267G06V10/457G06F18/2163G06F18/23213
Inventor 路小波李永彬
Owner SOUTHEAST UNIV
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