In this study, a novel and efficient deep learning model are proposed to estimate the number of people in highly dense crowd images. We present a convolutional neural network model consisting of two parallel modules which focus on various specific features of the images. Thus, while the general density map is derived by obtaining lower-level features from the first module, it is possible to identify regions of the human body, such as head and upper body with the help of the higher-level features in the deeper second module. These two modules are then concatenated with a fully connected neural network. The proposed model was tested with the ShanghaiTech Part-A dataset. The mean square error and mean absolute error values are used as performance metrics. By comparing these metrics regarding recent studies, more successful results were obtained by using the proposed method.
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Deep and Wide Convolutional Neural Network Model for Highly Dense Crowd
Semantic Scholar · Computer Science · 2019
Abstract
In this study, a novel and efficient deep learning model are proposed to estimate the number of people in highly dense crowd images. We present a convolutional neural network model consisting of two parallel modules which focus on various specific features of the images. Thus, while the general density map is derived by obtaining lower-level features from the first module, it is possible to identify regions of the human body, such as head and upper body with the help of the higher-level features in the deeper second module. These two modules are then concatenated with a fully connected neural network. The proposed model was tested with the ShanghaiTech Part-A dataset. The mean square error and mean absolute error values are used as performance metrics. By comparing these metrics regarding recent studies, more successful results were obtained by using the proposed method.