Crowd counting method based on multi-scale context enhancement network

Abstract

The invention provides a crowd counting method based on a multi-scale context enhancement network, which comprises the following steps: inputting a picture, obtaining shallow features and deep features after feature extraction, performing feature fusion through a feature fusion module, sending the fused features into a multi-scale perception module, and finally, encoding the space and channel information of the features through a context enhancement module to obtain a density map with crowd distribution features. The estimated number of people in the current picture can be obtained by summingthe density image pixels. According to the crowd counting method based on the multi-scale context enhancement network, the multi-scale problem existing in crowd counting can be effectively solved, andmore accurate counting and density estimation can be carried out on crowds in a complex scene by modeling space and channel context information of a feature map. The invention is high in robustness,and can provide accurate data for the safety and planning of a large crowd gathering place.

Description

technical field [0001] The invention relates to a crowd counting method based on a multi-scale context enhancement network. Background technique [0002] The main task of crowd counting is to estimate the number and density distribution of people in an image or video frame. Accurate crowd counting and density estimation can help people effectively avoid stampedes and riots caused by highly crowded crowds. [0003] In recent years, thanks to the improvement of GPU computing power and the emergence of large-scale crowd data sets, deep learning methods have been widely used in the field of crowd counting, and methods based on convolutional neural networks have made significant progress in crowd counting tasks in complex scenes. . [0004] However, in real scenes, crowd counting tasks, like other computer vision tasks, face many challenges, such as uneven distribution of crowds, changes in scale and perspective, and mutual occlusion, making crowd counting tasks extremely diffi...

AI Technical Summary

Problems solved by technology

[0004] However, in real scenes, crowd counting tasks, like other computer vision tasks, face many challenges, such as uneven distribution of crowds, changes in scale and perspective, and mutual occlusion, making crowd counting tasks extremely difficult. challenge

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