Multi-Stream Networks and Ground Truth Generation for Crowd Counting


  • Rodolfo Quispe University of Campinas, Institute of Computing
  • Darwin Ttito University of Campinas, Institute of Computing
  • Adín Rivera University of Campinas, Institute of Computing
  • Helio Pedrini University of Campinas, Institute of Computing



Crowd Counting, Deep Learning, Density Maps, Multi-Stream Network


Crowd scene analysis has received a lot of attention recently due to a wide variety of applications, e.g., forensic science, urban planning, surveillance and security. In this context, a challenging task is known as crowd counting [1–6], 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 paper, 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 the use of our ground truth generation methods achieves superior results.






Original Scientific Papers