Shared bicycle parking detection method and system based on video progressive region extraction

Abstract

The invention discloses a shared bicycle parking detection method based on video progressive area extraction, which comprises the following steps of: carrying out attention area extraction processingon a video image to obtain a background image; clustering the length and width of a target object in a training set image; according to the clustering results, adjusting the regional generatgion network parameters of a regional convolutional neural network, and extracting the candidate regions of the target object by the regional convolutional neural network. In the process of adopting the targetdetection technology, the invention utilizes the existing well-deployed monitoring video system to adjust the region generation network parameters of the region convolution neural network of the clustering result of the target object by using a clustering method, so that the position and classification of the target object can be accurately obtained, and the management standard of the target object can be facilitated. The shared bicycle parking detection method and system based on the video progressive region extraction of the invention can be widely applied in the field of image processing.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a shared bicycle parking detection method and system based on video progressive region extraction. Background technique [0002] With the rapid development of shared bicycles, parking chaos has become one of the biggest pain points of current urban management. As a common method to deal with this problem, the manual inspection method has a large workload, is not targeted, and it is difficult to find scattered bicycles that are not on main roads. In the parking problem, finding shared bicycles parked randomly is the first and key step. [0003] In the related technologies of shared bicycle detection, it is mainly divided into two categories, one is manual detection, through manual viewing of videos or real scenes, it is found whether there is any disorderly parking of public bicycles; the second is mainly through sensing equipment, using electronic Fences and other technical means...

AI Technical Summary

Problems solved by technology

The electronic fence based on satellite positioning technology (GPS) solves the huge workload of manual observation, and can also detect the shared bicycles more accurately. However, due to the limited GPS positioning accuracy and communication power consumption, it is impossible to accurately determine whether the shared bicycles are It is parked in the specified parking area; the electronic fence based on the Bluetooth electronic tag needs to install the Bluetooth device in the parking spot and the car lock to provide the corresponding network and power supply services, increase the cost of installation and maintenance, consume a lot of material resources, and dig Pit poles involve municipal planning and management issues, and are affected by positioning accuracy, resulting in large positioning deviations

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