Crowd density estimation and people counting method based on full convolutional network

Abstract

The invention discloses a crowd density estimation and people counting method based on a full convolutional network. A training data preparation module, a prediction model designing module, a prediction model training module and a real-time detection module are included; the full convolutional network comprises a deep layer convolutional neural network and two shallow layer convolutional neural networks; the deep layer convolutional neural network is used for processing scenes close to cameras of the crowd, human face and body features are obtained, and the maximum pooling operation is adopted; the shallow layer convolutional neural networks are used for processing the scenes far from the camera of the crowd, and obtaining body contour information and adopts the average pooling operation.The model adopts the full convolutional network and is applicable to input images of any size; due to the mode of deep layer and shallow layer network combination adopted by the model, the model can be migrated to different application scenarios; the system can efficiently and accurately predict the crowd density and the crowd quantity.

Description

technical field [0001] The invention belongs to the technical field of computer vision technology and artificial neural network, and in particular relates to a method for crowd density estimation and population counting based on a fully convolutional network. Background technique [0002] In recent years, with the development of the economic level and the rapid population growth, riots caused by crowd gathering have occurred more than once, and crowd monitoring has become more and more important. However, relying on manpower to achieve crowd monitoring is prone to fatigue and is susceptible to personal subjective factors. At the same time, computer vision technology is becoming more and more mature, and its application scope in engineering has been extended to all aspects of life such as license plate recognition, face detection, fingerprint recognition, etc., thus further promoting the research on automatic estimation methods of crowd density. [0003] Crowd density level e...

AI Technical Summary

Problems solved by technology

[0011] (2) Integrating a large number of artificial features, the design features are complex, and the steps to use are cumbersome;

[0012] (3) It is impossible to obtain the density estimation of the crowd and the specific number of the crowd at the same time;

[0013] (4) The steps of calibrating real samples are cumbersome

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