Dense crowd counting method, device and equipment and storage medium

Abstract

The invention discloses a dense crowd counting method and device, equipment, and a computer readable storage medium. The method comprises the steps of inputting a to-be-tested image into a target multi-scale multi-column convolutional neural network model comprising a multi-column parallel convolutional neural network, wherein each column of convolutional neural network comprises a plurality of convolutional layers with different convolutional kernel sizes and numbers; processing a to-be-tested image by using each convolution layer in each column of convolutional neural networks, and fusing the feature maps output by the pre-selected convolution layers in each column of convolutional neural networks to obtain an estimated density map output by each column of convolutional neural networks;fusing the estimated density maps output by each column of convolutional neural networks to obtain a target estimated density map of the to-be-tested image; and calculating the number of people in theto-be-tested image according to the target estimation density map. According to the method, the device, the equipment and the computer readable storage medium provided by the invention, the accuracyof the dense crowd image prediction result is improved.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method, device, equipment and computer-readable storage medium for counting dense crowds. Background technique [0002] Accurately estimating crowds from images or videos has become an increasingly important application of computer vision techniques for crowd control and public safety. The task of crowd counting in computer vision is to automatically count the number of people in an image or video. To aid in crowd control and public safety in many scenarios such as public gatherings and sporting events, accurate crowd counting is required. [0003] Traditional dense crowd counting methods include two types: detection-based methods and regression-based methods. Detection-based methods treat a crowd as a set of detected individual entities. However, pedestrians are often occluded by dense crowds, which is especially challenging when estimating crowds in still i...

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention is to provide a method, device, equipment and computer-readable storage medium for counting dense crowds, so as to solve the problem of poor performance of the neural network for counting dense crowds provided in the prior art

Images