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Statistical method, system, computing device and storage medium for bus operation data

A technology for data statistics and public transportation, applied in the computer field, can solve the problems of high cost and low data accuracy, and achieve the effect of ensuring accuracy, reducing equipment deployment, and low deployment requirements

Active Publication Date: 2022-03-04
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a bus operation data statistics method, system, computing equipment and storage medium, aiming at solving the problems of low data accuracy and high cost existing in the prior art

Method used

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  • Statistical method, system, computing device and storage medium for bus operation data
  • Statistical method, system, computing device and storage medium for bus operation data
  • Statistical method, system, computing device and storage medium for bus operation data

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Experimental program
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Embodiment 1

[0047] figure 1 The implementation process of the bus operation data statistical method provided by the first embodiment of the present invention is shown. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0048] In step S101, a number of video images to be processed are obtained. The video images to be processed are obtained by processing the bus environment video, and there is a time sequence relationship between different video images to be processed.

[0049] In this embodiment, when bus operation data such as passenger flow data and passenger congestion data need to be counted for a certain bus environment, a video shooting system can be built in the bus environment or an existing video shooting system for monitoring can be used. The shooting system can use ordinary vertical hanging cameras to shoot corresponding bus scenes. The real-time video stream or offline video files ob...

Embodiment 2

[0058] On the basis of Embodiment 1, this embodiment further provides the following content:

[0059] Such as figure 2 As shown, before step S101, it also includes:

[0060] In step S201, a frame cutting process is performed on the bus environment video to obtain an original video image.

[0061] In this embodiment, during the frame cutting process, there is an appropriate time interval between the cut out original video images (frames), which should not be too short to avoid repeated images.

[0062] In step S202, the original video image is preprocessed to obtain a video image to be processed.

[0063] In this embodiment, in order to enable the subsequent deep learning network to input the required video images to be processed, it is necessary to perform corresponding preprocessing on the original video images, and the preprocessing may include one or more of the following processing methods: First, the denoising process can use three-dimensional block matching (Block Ma...

Embodiment 3

[0065] This embodiment further provides the following content on the basis of other embodiments:

[0066] The deep learning network used in step S102 is preferably an SSD deep learning network.

[0067] In this embodiment, the SSD deep learning network can be obtained through more than 200,000 rounds of sample training based on the tensorflow framework of the python language. The learning rate can be set to 0.0005 for the first 50,000 rounds of training. During the 10,000th round and the 150,000th round of training, the learning rate is multiplied by 0.5, 0.1, and 0.05 to further reduce the learning rate, and the visual geometry group (Visual Geometry Group, VGG)-16 is used to train the model based on the ImageNet dataset. Migration learning is carried out to finally obtain the SSD deep learning network referred to in this embodiment.

[0068] The SSD deep learning network architecture can be as follows image 3 As shown, it includes: a pre-convolutional network 301 and a po...

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Abstract

The present invention is applicable to the field of computer technology, and provides a bus operation data statistical method, system, computing equipment and storage medium. By obtaining a number of video images to be processed, the video images to be processed are obtained by processing the video of the bus environment. There is a temporal relationship between the video images; use the deep learning network to detect passengers in the video images to be processed; determine the location information of the passengers at different time points according to the detection results and obtain the passenger's movement trajectory; based on the passenger's movement trajectory, count the bus operation data. In this way, the deep learning network is used to detect the passengers in the image, and track the passengers to obtain the passenger movement trajectory, and then count the bus operation data, so that the conventional image can be processed, so the deployment requirements of the front-end sensor devices are lower, reducing the Equipment deployment and maintenance costs, and the deep learning network can accurately process images, thereby ensuring the accuracy of statistical data.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a bus operation data statistics method, system, computing equipment and storage medium. Background technique [0002] With the rise of artificial intelligence algorithms represented by deep learning algorithms, research fields such as image processing, image recognition, language signal processing, and natural language processing have developed rapidly. [0003] In terms of bus passenger flow and other operational data statistics, the current commonly used methods are mainly to use infrared sensors or pressure sensors to obtain front-end data, and then process the front-end data to obtain bus operation data. However, when the front-end data is obtained based on infrared sensing or pressure sensing, it is easy to cause misjudgment, the accuracy of the obtained data cannot be guaranteed, and the deployment is relatively complicated, the equipment is fragile, and the m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q50/30G06N3/04G06V20/40
CPCG06Q50/30G06V20/40G06N3/045
Inventor 张勇涂文涛赖颖昕张席何钦煜
Owner SHENZHEN UNIV
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