Embedded crowd density estimation method based on a convolutional neural network model

A convolutional neural network and crowd density technology, applied in the field of convolutional neural network models for embedded crowd density estimation, can solve problems such as unrealistic and real-time demand changes, and achieve high accuracy and few parameters The effect of summing the amount of computation and reducing the amount of computation

Active Publication Date: 2019-04-12
SUN YAT SEN UNIV
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Problems solved by technology

However, when the model is applied to embedded devices, the performance of the device, the size of the imaging, and the real-time requirements of customers may change. It is impractical to design networks according to different situations.

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  • Embedded crowd density estimation method based on a convolutional neural network model
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  • Embedded crowd density estimation method based on a convolutional neural network model

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[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0039] It should be noted that if there is a directional indication (such as up, down, left, right, front, back...) in the embodiment of the present invention, the directional indication is only used to explain the position in a certain posture (as shown in the accompanying drawing). If the specific posture changes, the directional indication will also change accordingly.

[0040] In addition, if there are descriptions involving "first", "second" and so...

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Abstract

The invention discloses an embedded crowd density estimation method based on a convolutional neural network model and the convolutional neural network model of embedded crowd density estimation. The model is used for realizing the method. The method comprises the following steps of: nesting structures of three convolutional neural branches with the output capability of generating a crowd density map; the model has three operation modes; and after the training image is preprocessed, training a convolutional neural network model, inputting the image to the trained convolutional neural network model, selecting one of the three operation modes, outputting a crowd density map corresponding to the selected mode, and performing an integral operation on the output density map to obtain the total number of people of the image. The convolutional neural network model is light in weight, the accuracy is higher than that of a convolutional neural network model of the same magnitude, three deployment modes can be switched at will, the speeds of the modes are different, and the speeds can be selected according to actual conditions.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an embedded crowd density estimation method based on a convolutional neural network model and a convolutional neural network model for embedded crowd density estimation. Background technique [0002] Relying on embedded real-time computing for localization can overcome the shortcomings of existing methods based on convolutional neural networks. Comparison of server-side GPU-based and embedded-based crowd density estimation methods image 3 As shown, in comparison, the latter is more convenient and easy to use and helps to save costs. However, when the model is applied to embedded devices, the performance of the device, the size of the image, and the real-time requirements of customers may change. It is impractical to design networks according to different situations. [0003] For this reason, we need to design a lightweight and speed-adjustable convolutional neural network model f...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/53G06F18/214
Inventor 陈景宇谢晓华郑伟诗
Owner SUN YAT SEN UNIV
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