Illegal parking detection method based on image texture

Abstract

The invention discloses an illegal parking detection method based on image texture. The method sequentially includes the following steps of obtaining the background, segmenting targets and analyzing the texture. The background obtaining is mainly achieved through the regional division of a determined detection area and a detection area, the establishment of a detection area model and the background renewing. The target segmenting is mainly achieved through the dynamic threshold value selection, the target segmentation and the morphology filtering. By means of the target segmentation method where dynamic threshold values are adopted, moving targets can be more accurately obtained through segmentation; meanwhile, the noise interference generated in the segmentation process can be effectively eliminated through the opening operation in the morphology filtering; the texture analyzing is mainly achieved through the static target detection, the running and parked vehicle recognition and the illegal parking event conforming; whether static objects appear or not is judged by analyzing the process during which the image gray value reaches the steady state; meanwhile, the false alarm existing in the process is eliminated through the entropy of an image gray level co-occurrence matrix.

Description

technical field [0001] The invention belongs to the technical field of traffic detection, in particular to an image texture-based detection method for illegal parking. Background technique [0002] With the continuous acceleration of the urbanization process in our country, the pressure on the urban highway traffic system is increasing, and the traffic problem of illegal parking is becoming more and more prominent, which has a very bad impact on the maintenance of normal traffic order. How to regulate the driver's driving behavior and conduct timely and accurate evidence collection and investigation of violations of traffic rules is a key issue in road monitoring. [0003] The monitoring capabilities and monitoring effectiveness of the existing monitoring system are still basically at the stage of manual operation monitoring and evidence collection after the event, unable to play the role of prevention, early warning, and real-time monitoring throughout the day. far beyond ...

AI Technical Summary

Problems solved by technology

[0003] The monitoring capabilities and monitoring effectiveness of the existing monitoring system are still basically at the stage of manual operation monitoring and evidence collection after the event, unable to play the role of prevention, early warning, and real-time monitoring throughout the day. far beyond human capacity

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