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• W3043116150 abstract "In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting using fewer computer resources without a significant loss on count accuracy. The architecture was trained using the Bayes loss function to further improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF achieving a competitive Mean Average Error of 154.07 and a superior Mean Square Error of 241.77 while maintaining a competitive number of parameters of 0.067 Million. The obtained results suggest that the Bayes loss can be used with other architectures to further improve them and also the last convolutional layer provides no significant information and even encourage over-fitting at training." @default.
• W3043116150 created "2020-07-23" @default.
• W3043116150 creator A5054642793 @default.
• W3043116150 creator A5067582822 @default.
• W3043116150 date "2020-07-13" @default.
• W3043116150 modified "2023-10-01" @default.
• W3043116150 title "Dense Crowds Detection and Counting with a Lightweight Architecture." @default.
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• W3043116150 hasPublicationYear "2020" @default.
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