Dense crowd counting method based on multi-scale feature pyramid network

Abstract

The invention discloses a dense crowd counting method based on a multi-scale feature pyramid network, and aims to solve the problems of density map estimation and crowd counting in a complex and crowded scene. A feature pyramid fusion module is utilized to effectively capture multi-scale context information in the crowd image, and better feature expression is further obtained; focusing a high-density position in the crowd image by using a feature attention perception module to reduce background interference; restoring the image to the original size by using a bilinear difference value; according to the method, the problem that crowd counting is difficult due to large head scale change, serious crowd shielding and poor illumination conditions in a dense scene is effectively solved, the robustness of the model to noise is improved, accurate crowd counting and high-quality prediction of the density map can be achieved, and the crowd counting accuracy is improved. Due to the fact that the grouping convolution module is utilized, the reasoning speed is high.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for counting dense crowds based on a multi-scale feature pyramid network. Background technique [0002] Dense crowd analysis has important application value in video surveillance, traffic guidance, public safety prevention and control, and intelligent environment design. Common dense crowd analysis tasks mainly include crowd counting, crowd image segmentation, crowd detection and tracking, crowd behavior recognition and positioning, etc. Among them, crowd counting is a fundamental task in the field of dense crowd analysis. However, in real scenes, there are still problems such as large changes in the size of the head in the image due to different shooting angles and distances; in addition, there are also problems such as complex background changes and severe occlusion of the human body in crowded scenes. These problems pose great challenges to the current cr...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problems in the prior art, the present invention provides a method based on a multi-scale feature pyramid network and a dense crowd counting method, which can effectively solve the difficult problem of crowd counting and density map prediction in crowded scenes, and improve the robustness to noise. Rodness, enabling accurate counting of crowds and high-quality prediction of density maps

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