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Deep-convolution-neural-network-based method for detecting illegal parking and converse running of vehicles

A deep convolution and neural network technology, applied in the field of deep feature video detection, can solve the problem of inability to effectively manage existing video resources, and achieve the effect of saving hardware investment, low cost and low price

Active Publication Date: 2017-06-20
NANJING UNIV
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Problems solved by technology

[0003] The problem to be solved by the present invention is: as the number of road monitoring videos increases sharply, it is impossible to achieve effective management of existing video resources only by manual work

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  • Deep-convolution-neural-network-based method for detecting illegal parking and converse running of vehicles

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Embodiment Construction

[0028] The invention introduces deep learning into road event recognition and improves it, which can significantly improve the accuracy of road event recognition. Considering that vehicles are static targets, traditional background modeling methods are not suitable for static target detection, and road remnants are difficult to use a priori model to construct a training set, so that road remnants that are also static targets will be confused with illegally parked vehicles . The present invention establishes a road surface reverse recognition model based on a deep convolutional neural network Deep-CNN. The detection point of the mobile terminal is a road camera, and the detection point of the mobile terminal obtains image information through the camera, and the CNN neural network is used to analyze the acquired image. The road ROI area is divided into multiple networks, and a road-non-road recognition model is constructed to reversely identify illegal parking and reverse drivin...

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Abstract

The invention relates to a deep-convolution-neural-network-based method for detecting illegal parking and converse running of vehicles. A mobile terminal detection point is used as a road camera. The mobile terminal detection point obtains image information by the camera and depth learning is introduced into road event identification and improvement is also carried out, so that the accuracy of road event identification can be improved obviously. According to the method provided by the invention, a convolution neural network is used for analyzing an obtained image; a pavement ROI region is divided into a plurality of networks; a pavement-non-pavement identification model is constructed; and reverse identification of illegal parking and converse running of vehicles at highways is realized by using a non-pavement grid. The method is applied to non-real-time tasks like pavement illegal parking detection and converse running detection of vehicles. With full utilization of advantages and characteristics of the mobile internet network, high-coverage pavement event detection including illegal parking and converse running of vehicles is realized with low cost.

Description

technical field [0001] The invention belongs to the technical field of video detection of depth features, and relates to the detection of road surface events. It is based on the Deep-CNN road surface reverse recognition model to detect illegal parking or retrograde vehicles, as well as data analysis and data mining of the detection results. It is a depth-based A Convolutional Neural Network (Deep-CNN) Vehicle Violation Parking and Retrograde Detection Method. Background technique [0002] Road accidents, traffic congestion, and environmental pollution are common problems faced by the development of today's road traffic. Road traffic safety is worrisome. Road informatization and Intelligent Transportation System (ITS) are effective means to improve the utilization efficiency of road facilities, alleviate traffic congestion, and reduce the incidence of traffic accidents. Use computer vision technology to sense traffic flow parameters such as road speed and flow in real time, ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04
CPCG06V20/41G06V20/54G06V10/25G06N3/045G06F18/214
Inventor 阮雅端高妍赵博睿陈金艳陈启美
Owner NANJING UNIV
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