Deep learning-based image high-density population counting method

Abstract

The invention discloses a deep learning-based image high-density population counting method. The method comprises the following steps of S1, establishing a depth complementation convolutional neural network by utilizing a deep learning framework caffe; S2, performing image data enhancement on an image according to operations of angle rotation, image multi-scale zooming, image mirroring and image pyramid zooming; S3, performing Gaussian kernel fussy normalization processing on the enhanced image data to obtain a real crowd density graph, outputting an estimated density graph and the real density graph by the network, and performing continuous iterative training optimization on the whole network structure according to a loss function; and S4, inputting a crowd picture and a tag picture to the network for training, and performing continuous iterative optimization to obtain a trained network model finally. According to the method, the end-to-end convolutional neural network is designed; a picture is given and input, and the estimated density graph corresponding to the picture is output, so that an estimated crowd number is obtained; and by outputting the density graph, more useful information is reserved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for counting high-density crowds based on deep learning images. Background technique [0002] In recent years, the development of deep learning has been very hot. Convolutional neural networks have developed rapidly in image processing. Neural networks of various architectures emerge in endlessly. By designing sophisticated neural network structures, they can be used to estimate the number of people in high-density scenes. In public places such as train stations, gymnasiums and other places with dense traffic, it is of great significance to improve public safety by monitoring the number of people in real time and regulating the flow of people so as to avoid incidents that threaten personal safety such as stampedes. [0003] Traditional crowd counting algorithms require complex preprocessing of images in the early stage, manual design and feature extraction, and ...

AI Technical Summary

Problems solved by technology

[0003] Traditional crowd counting algorithms require complex preprocessing of images in the early stage, manual design and feature extraction, and poor adaptability in different scenarios. In high-density crowd scenarios, due to serious occlusion and perspective distortion, traditional Crowd counting algorithms are less effective

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