Dense group counting and positioning method based on distance transformation label

Abstract

The invention discloses a dense group counting and positioning method based on a distance transformation label. According to the invention, a novel real label generation mode is provided; the real label is named as a Distance label map. Through Distance label map, the estimated number of people can be obtained only by counting the number of the local minimum value areas, and besides, the specificposition information of the predicted human head can be obtained by calculating the center coordinates of the local minimum value areas. A feature pyramid network is adopted for feature extraction, and the picture is input into the network to obtain a plurality of different fusion features, and loss is calculated with the real label and summed; according to the method, a new function for calculating the loss is provided, the function is called as the self-adaptive cross entropy, the label of the pixel point can be used for representing the distance information between the pixel point and the nearest pixel point, and the traditional cross entropy is subjected to weighted improvement. Compared with the related work of the previous group counting, the positioning information of each head canbe provided while the counting accuracy is ensured.

Description

Technical field [0001] The invention relates to the field of computer vision, in particular to a method for counting and positioning dense groups based on distance transform tags. Background technique [0002] In recent years, there have been frequent stampede incidents when holding large-scale events at home and abroad. The main reason is that the specific number and location of the crowd cannot be obtained in time, and the flow of people cannot be effectively evacuated. This makes crowd counting work in video surveillance, crowd understanding, and public safety. Prevention and other fields have gradually played an important role, and have become a hot research issue in the field of computer vision. [0003] With the development of deep learning, researchers can use deep neural networks to achieve dense crowd counting. The existing crowd counting methods can generally be divided into two categories. The first is to use crowd counting as a target detection task, and view pedestrian...

AI Technical Summary

Problems solved by technology

[0004] The present invention proposes a dense group counting and positioning method based on distance transformation labels, which can provide specific location information of each head while ensuring counting accuracy, so as to solve the problem that the method based on regression density map in dense group scenes cannot provide Accurate positioning information, the problem that detection-based methods cannot be applied to dense crowd scenes

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