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Image target counting method based on convolutional neural network

A convolutional neural network and target counting technology, applied in image data processing, image enhancement, image analysis, etc., can solve the problem of difficult to meet the requirements of efficient processing, high computational complexity, and a large number of parameters in multi-column convolutional network models. and other problems, to achieve the effect of high practical application value and low computational complexity

Inactive Publication Date: 2018-11-30
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

However, in the above scheme, the multi-column convolutional network model has a large number of parameters and high computational complexity, which is difficult to meet the requirements of practical applications for efficient processing

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  • Image target counting method based on convolutional neural network
  • Image target counting method based on convolutional neural network
  • Image target counting method based on convolutional neural network

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[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some 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 making creative efforts belong to the protection scope of the present invention.

[0022] Natural scenes are usually complex and changeable. For image target counting tasks, it is easily affected by various factors, such as severe occlusion between targets, target deformation, uneven distribution of targets, messy background interference, distortion of camera perspective, etc. . In particular, the influence of the perspective effect of the camera makes the size of the same object vary in different depths of the scene, and the cam...

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Abstract

The invention discloses an image object counting method based on a convolutional neural network. The image object counting method makes the characteristics learned by the network robust to the targetdeformation through a robust enhancement layer, and lowers the computational complexity of the model; a pyramid layered counting module is used to perform density estimation, and the multi-scale information contained in hierarchical features of the convolutional neural network are fully used, which significantly improves the computational efficiency while accurate counting is achieved. In summary,the image object counting method is based on the convolutional neural network, achieves accurate counting of the target in an image, and can be applied to a target counting task in a complex scene, and is low in computational complexity and high in practical application value.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an image target counting method based on a convolutional neural network. Background technique [0002] With the rapid development of computer technology, network communication technology and electronic technology and the continuous improvement of people's requirements for social public security, the intelligent video surveillance system based on intelligent video analysis technology has been widely used. As an important content in the field of intelligent video surveillance, target counting has a large number of application scenarios in real life. Accurately estimating the specific number of targets in the image is the key to related system processing. In the intelligent transportation system, accurately estimating the number of vehicles in the traffic scene can provide an important basis for the traffic management department to manage public transportation; statistics on...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/20081G06T2207/20084G06T2207/30242
Inventor 王子磊刘旭
Owner UNIV OF SCI & TECH OF CHINA
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