  Crowd scene analysis has received a lot of attention recently due to the wide
variety of applications, for instance, forensic science, urban planning,
surveillance and security. In this context, a challenging task is known as
crowd counting, whose main purpose is to estimate the number of people present
in a single image. A Multi-Stream Convolutional Neural Network is developed and
evaluated in this work, which receives an image as input and produces a density
map that represents the spatial distribution of people in an end-to-end
fashion. In order to address complex crowd counting issues, such as extremely
unconstrained scale and perspective changes, the network architecture utilizes
receptive fields with different size filters for each stream. In addition, we
investigate the influence of the two most common fashions on the generation of
ground truths and propose a hybrid method based on tiny face detection and
scale interpolation. Experiments conducted on two challenging datasets,
UCF-CC-50 and ShanghaiTech, demonstrate that using our ground truth generation
methods achieves superior results.
