Method, device, equipment and storage medium for determining the number of people

Abstract

The present application relates to a method, device, equipment and storage medium for determining the number of people, and relates to the technical field of image processing. The method includes: acquiring a first image; performing feature extraction based on the first image to obtain a crowd density feature map corresponding to the first image; based on at least two population classification intervals, respectively classifying the crowd density feature map; based on the first Each sub-area in the image corresponds to a sub-interval in at least two number classification intervals, and the number of people information corresponding to each sub-area in the first image is obtained; based on the number of people information corresponding to each sub-area in the first image, Acquire the number of people corresponding to the first image. The above scheme can be applied in the field of intelligent transportation. According to the classification situation of the first image in two population classification intervals with different sub-intervals, the number of people in each area on the first image is estimated, which improves the estimation of the number of people in the image. Accuracy, improving the accuracy of intelligent traffic dispatching.

Description

technical field [0001] The present application relates to the field of computer vision, and in particular to a method, device, equipment and storage medium for determining the number of people. Background technique [0002] Crowd density estimation, an application that automatically infers the total number of people in an image, plays an important role in video surveillance, public safety, and more. [0003] In related technologies, the traditional methods based on detection and direct regression, most of the crowd density estimation algorithms at this stage combine heat map regression, and use deep learning technology for end-to-end training and reasoning, which can better solve the crowd density distribution range. The problem of large scale and large variation range of head scale improves the counting accuracy to a certain extent. And in the process of identifying the number of people in the image, the image blocks can be divided into different categories according to th...

AI Technical Summary

Problems solved by technology

[0004] In the above scheme, when the image blocks are divided into different categories, the predicted number of people will be uniformly set as the proxy count value of the corresponding interval, and the discretization error is large, which affects the accuracy of predicting the number of people in the image

Images