Bus passenger flow statistics method based on monocular camera and deep learning technology

A technology of deep learning and statistical methods, applied in computing, computer parts, character and pattern recognition, etc., can solve the problems of large deep learning model, low statistical accuracy, complex calculation, etc., and achieve high precision and high statistical accuracy. efficiency, reducing the storage space and the amount of computation

Pending Publication Date: 2019-11-29
杭州律橙电子科技有限公司
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Problems solved by technology

The existing bus passenger flow statistics generally have the shortcomings of low

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  • Bus passenger flow statistics method based on monocular camera and deep learning technology
  • Bus passenger flow statistics method based on monocular camera and deep learning technology
  • Bus passenger flow statistics method based on monocular camera and deep learning technology

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[0035] The present invention will be further described below in conjunction with the drawings and embodiments.

[0036] The bus passenger flow statistics method based on the monocular camera and deep learning technology of this embodiment includes the following steps: S1, video capture, using a video recorder to collect videos at the front and rear doors of the bus and obtain the original video; S2, sample labeling, The video is screened, screenshots, and annotated, and training samples are obtained; S3, fitting model, training the training samples and fitting the model, and obtaining a deep learning model; S4, lightweight processing, lightening the deep learning model in step S3 Quantify processing, and then proceed to step S5; S5, run the deep learning model and count, so as to count the bus passenger flow. Such as figure 1 Shown.

[0037] In this embodiment, the lightweight processing includes first pruning the data of the deep learning model and retaining the weights after pru...

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Abstract

The invention discloses a bus passenger flow statistics method based on a monocular camera and a deep learning technology, and the method comprises the following steps: S1, video collection: collecting a video at a front door and a rear door of a bus through a video recorder, and obtaining an original video; s2, sample annotation: performing screening, screenshot and annotation on the original video to obtain a training sample; s3, model fitting: training the training sample and fitting the model to obtain a deep learning model; s4, light weight processing: carrying out light weight processingon the deep learning model in the step S3, and then carrying out the step S5; and S5, running the deep learning model and counting, thereby counting the bus passenger flow. The bus passenger flow statistical method based on the monocular camera and the deep learning technology overcomes the defects of low statistical accuracy, high cost, large deep learning model and complex operation in the prior art, and also has the advantages of reducing the storage space and operation amount of the deep learning model, being low in cost and the like.

Description

technical field [0001] The invention relates to the technical field of bus passenger flow statistics, in particular to a method for bus passenger flow statistics based on a monocular camera and deep learning technology. Background technique [0002] Transportation is an important basis for the production and life of human society and the development of urban economy. The public transport system is an integral part of urban transportation. With the development of artificial intelligence technology in recent years, smart public transportation has attracted more and more attention from the public and the government. Smart bus is a brand-new product that integrates bus with smart devices and the concept of mobile Internet, which can greatly improve travel efficiency and allow people to fully enjoy the smart urban life brought about by information technology. The bus passenger flow statistics system can help managers understand the operating status of vehicles at any time, impr...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/53G06F18/40G06F18/214
Inventor 王杰金鹏
Owner 杭州律橙电子科技有限公司
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