Depth embedded and dense dilated convolutional network for crowd density estimation

Nguyen, M. N.; Tran, V. H.; Huynh, T. N.

In recent years, due to the rapid growth of the urban population, the management of public security has become extremely necessary. Therefore, accurate crowd counting and density distribution estimation play an important role in many situations especially during the Covid-19 pandemic which has been spreading around the world. Although many studies have been proposed, it remains to be a challenging task because of the vivid intra-scene scale variations of people caused by depth effects. In this paper, we propose a novel unified system that allows the scale variation problem to be solved both directly and indirectly. To allow the network to have an understanding of depth when estimating crowd density, we first propose to embed this information into the crowd density estimation network indirectly through the training process by mean of multi-task learning. Our network is now designed to solve not only the main task of estimating crowd density, but also a side task: depth estimation. Besides, to learn the large-scale features directly, dense dilated convolution blocks were proposed to be used in our encoder. The experimental results demonstrate that by using both such direct and indirect methods, we can boost the performance and achieve good results compared to existing methods. Besides, with the multi-task design, we can completely cut off the unnecessary branches of the network related to the side task to speed up computation during the testing phase. © 2021 IEEE.