Method for detecting and counting crowd distribution in video based on deep learning

Abstract

The invention provides a method for detecting and counting crowd distribution in video based on deep learning. The method comprises the following steps of obtaining a large number of videos containing crowds with different densities, making a data set, wherein the data set comprises a training sample set, a test sample set and a real density map; establishing a deep neural network based on an attention mechanism; inputting the training sample set into a deep neural network, setting training parameters, training by using a loss function until the loss is reduced to a certain degree and the training reaches the maximum number of iterations, and generating a training model; inputting the test sample set into the trained model, outputting a crowd density map, and evaluating the performance of the model; and performing point clustering on the crowd density map by using a CFDP clustering method to identify the group, and quickly obtaining the number and position information of the group. According to the crowd distribution detection and technical method in the video based on deep learning, an area with crowds can be accurately detected, and the number of dense crowds can be estimated with high precision.

Description

technical field [0001] The invention relates to the technical field of video image processing, in particular to a method for detecting and counting crowd distribution in videos based on deep learning. Background technique [0002] In recent years, with the development of computer vision, intelligent video surveillance technology has gradually been applied in traffic safety, public security and other fields. Crowd distribution detection and counting is a research hotspot in the field of intelligent video surveillance, which has important social significance and market application prospects. For example, in public places where crowds tend to gather, crowd counting and distribution detection can give early warning of safety issues such as stampedes, and realize the reasonable allocation and scheduling of manpower, material resources, and resources; for urban public transportation systems, it can be flexibly dispatched according to changes in the number of passengers Vehicles c...

AI Technical Summary

Problems solved by technology

However, most of the existing methods do not fully consider the influence of the background, which will lead to misidentification, and give the same attention to all the crowd areas on the entire image, which does not conform to the characteristics of the human visual system.

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