Multi-Stream Networks and Ground Truth Generation for Crowd Counting

Autor(es)

Quispe, Rodolfo ^[1] ; Ttito, Darwin ^[1] ; Rivera, Adin ^[1] ; Pedrini, Helio ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251, BR-13083852 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS; v. 11, n. 1, p. 33-41, 2020.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/19947-6 - Desenvolvimento de arquiteturas de redes neurais Recurrente Convolucional para o reconhecimento de expressões faciais
• Beneficiário: Gerberth Adín Ramírez Rivera
• Modalidade de apoio: Auxílio à Pesquisa - Regular