Passenger flow counting method based on deep learning in vertical visual angle

A deep learning, vertical perspective technology, applied in computing, image data processing, computer components and other directions, can solve the problems of influence, difficult to extract foreground, optical flow consumes a lot of computing power, etc., to improve competitiveness and improve scientificity , the effect of strengthening security

Active Publication Date: 2017-08-29
SYSU CMU SHUNDE INT JOINT RES INST +1
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  • Application Information

AI Technical Summary

Problems solved by technology

But the disadvantage of this feature is that it is easily affected by other objects with similar outlines, such as balloons, etc.
In the prior art, an effective pedestrian counting method is also proposed. For the first time, Hough circle Transform is used for head detection, followed by optical flow (optical flow) method for tracking. However, this method The disadvantage is that optical flow requires a lot of computing power and is difficult to deploy on embedded devices.
In the prior art, it is also proposed to first use Gaus

Method used

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  • Passenger flow counting method based on deep learning in vertical visual angle
  • Passenger flow counting method based on deep learning in vertical visual angle
  • Passenger flow counting method based on deep learning in vertical visual angle

Examples

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Example Embodiment

[0041] Example 1

[0042] Such as figure 1 As shown, a passenger flow counting method based on deep learning from a vertical perspective includes the following steps:

[0043] Step 1: Draw in and out statistical lines in the video screen:

[0044] The incoming and outgoing lines should be drawn as close as possible, and drawing in the middle of the screen will make the flow statistics more accurate. Such as image 3 As shown, the entry line is red, and the exit line is green. They are all drawn at the entrance of the elevator, and they are all drawn at the center of the picture.

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Abstract

The invention provides a passenger flow counting method based on deep learning in a vertical visual angle. According to the method, passenger flow statistics is performed in the vertical visual angle. Compared with an oblique photographing visual angle, the visual angle realizes easier coping with conditions such as market, supermarket and subway with high passenger density. The method provides head and shoulder detection by means of deep learning. Strong learning capability of deep learning is utilized. Background modeling and foreground extraction on a video are not required, and passenger cutting on the foreground is not required. Head information and shoulder information can be more accurately detected with higher robustness. A deep convolutional characteristic is utilized for performing matched tracking. Compared with manually designed characteristics such as HOG and LBP, the deep convolutional characteristic has better expression capability and can better cope with various scenes. The passenger flow counting method directly use the deep convolutional characteristic of a certain layer for matching, thereby preventing repeated characteristic calculation and realizing high time saving effect.

Description

technical field [0001] The present invention relates to the field of digital image processing, and more specifically, to a passenger flow counting method based on deep learning in a vertical perspective. Background technique [0002] In recent years, video passenger flow counting technology has been a research hotspot that has attracted much attention in the industry. It has also been gradually applied to major shopping malls, chain stores, supermarkets, hotels, airports, subways, scenic spots, etc. The people flow data generated in these scenarios can be used in many fields. Provides valuable information. For major shopping mall chain stores and supermarkets, facing the current hot online e-commerce systems, such as Jingdong, Taobao, Tmall, Amazon, etc., the offline sales market has been crowded, and scientific management is obviously to improve their own effective means of competitiveness. The flow of people data in different time periods and regions in the shopping mall...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T7/00
CPCG06T7/0002G06T2207/30242G06T2207/10016G06T2207/20081G06T2207/20084G06V20/53
Inventor 赖剑煌李传俊谢晓华
Owner SYSU CMU SHUNDE INT JOINT RES INST
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