1 research outputs found
Dense Crowds Detection and Counting with a Lightweight Architecture
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