Deep convolution neural network-based traffic flow density estimation method

A convolutional neural network, traffic density technology, applied in biological neural network models, neural architectures, counting of randomly distributed items, etc., can solve the problems of inability to obtain the number of vehicles, lack of vehicle density detection accuracy and quantitative analysis, etc. Achieve the effect of avoiding background modeling difficulties, easy learning, and avoiding inaccurate features

Active Publication Date: 2017-05-10
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

Although the invention application does not require the extraction of vehicle targets, it cannot obtain the specific number of vehicles at the same t

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  • Deep convolution neural network-based traffic flow density estimation method
  • Deep convolution neural network-based traffic flow density estimation method
  • Deep convolution neural network-based traffic flow density estimation method

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Embodiment Construction

[0048] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0049] The method for estimating traffic density and counting vehicles based on a deep convolutional neural network, based on a large number of samples of different traffic densities, uses a convolutional neural network to automatically extract feature maps with a high degree of discrimination, and multi-scale pyramidal image blocks input multiple The scale convolutional neural network forms an image density and vehicle counting model. By selecting the region of interest, the instantaneous traffic density of a fixed-length interval can be calculated, which greatly improves the accuracy and real-time performance of traffic density detection. In the training phase, train multiple convolutional neural networks with input images of different scales, learn the essential characterist...

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Abstract

The present invention provides a deep convolution neural network-based traffic flow density estimation method. The method comprises the steps of collecting road video images by a camera, sending multi-scale pyramid image blocks into a convolution neural network through image pre-processing, extracting simple features and abstract features from a bottom layer to a high layer, and obtaining a distribution density image of each scale of traffic flow image; learning a mapping from the multi-scale distribution density image to a distribution density image of an overall image and a number of all vehicles in the image by using a fully connected network layer; dividing regions of interest of a distribution density image of a video image output by the convolution neural network, summing pixels of the regions of interest so as to obtain a number of vehicles of a single lane or multiple lanes; and calculating according to a region length so as to obtain an instantaneous traffic flow density of the region. Through adoption of the method, vehicles are calculated and traffic flow density is estimated in real time more accurately.

Description

technical field [0001] The invention relates to a method for estimating traffic flow density based on a deep convolutional neural network, which belongs to the technical field of intelligent transportation. Background technique [0002] With the development of science and technology and the improvement of people's living standards, cars have become an indispensable means of transportation for people to travel and transport. All kinds of motor vehicles can be seen everywhere on the road. While automobile traffic makes people feel the convenience and comfort of modern life, it also reflects the lag and limitation of road facilities. Traffic congestion will bring a series of problems that cannot be ignored, such as long waiting time for vehicles, traffic accidents, and environmental pollution. In order to solve the above problems, intelligent transportation came into being. Vehicle counting and traffic density detection are important contents of intelligent transportation. T...

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Application Information

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IPC IPC(8): G06M11/00G06K9/00G06K9/32G06K9/34G06N3/04
CPCG06M11/00G06N3/04G06V20/52G06V10/25G06V10/267
Inventor 康宇魏梦宋卫国曹洋袁璟
Owner UNIV OF SCI & TECH OF CHINA
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