Zebra stripe detection method based on maximum stable region and random sampling

Abstract

The invention discloses a zebra stripe detection method based on a maximum stable extreme region (MSER) and random sampling consistency (RANSAC), belongs to the technical field of mode identification, and particularly relates to a detection and division method of a zebra stripe region in a traffic monitoring scene. The zebra stripe detection method comprises the following steps: firstly, carrying out background extraction on a traffic monitoring video by applying a multi-frame middle value method to reduce the shielding of zebra stripes by vehicles on the road surface; secondly, pre-processing a background image, and utilizing an MSER method to carry out characteristic extraction on the zebra stripes in the image; and finally, selecting an expanded RANSAC algorithm to screen zebra stripe key points from the image treated by the MSER, carrying out length and width fitting on an extracted zebra stripe region, displaying in an original image, and detecting the zebra stripe region. The zebra stripe detection method has the advantages of large detection angle range, far distance and strong adaptability.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a method for detecting and dividing a zebra crossing area in a traffic monitoring scene. Background technique [0002] The intelligent transportation system is the development direction of the future transportation system, and the zebra crossing detection is an important part of the intelligent transportation system. Zebra crossing detection can be used for environmental perception in traffic monitoring scenarios, including road area detection, traffic flow monitoring, vehicle pedestrian detection and other fields. Zebra crossing detection has gradually become a research hotspot in the past ten years. Due to the occlusion or light changes of the actual road surface and the unknown angle and number of zebra crossings, how to accurately judge whether the road surface is zebra crossing and find out the area where the zebra crossing is located is a difficult ...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to address the shortcomings of the zebra crossing detection and segmentation algorithm in the traffic monitoring scene that cannot adapt to multi-illumination environments, cannot automatically identify the number of zebra crossings, and has poor adaptability to the angle of zebra crossings, and proposes a detection angle based on the maximum stable area and random sampling Zebra crossing detection method with large range, long distance and strong adaptability

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