Dynamic regional population counting and localization method based on high resolution fusion

Abstract

Aiming at the problem of inaccurate crowd counting and location in dense scenes, a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed. Firstly, U-HRNet was used as the main backbone to extract high-resolution features of the population and enhance the ability of feature extraction with different resolutions. Then, the dynamic regional awareness attention module was designed to make full use of the global and local feature information, refine the differentiated learning of target feature and background feature, reduce the interference of background feature, and improve the positioning performance of the model. Finally, the predicted threshold map and confidence map were input into the binarization module to output the prediction and counting results of the crowd independent individual target. Experimental results showed that the proposed method achieved good performance of counting and positioning in different scenarios.

Keywords

deep learning / dense crowd count / high-resolution fusion / dynamic regional awareness / crowd location

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