Contagem de Fluxo de Pessoas Utilizando Aprendizado Profundo

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: PEDRO HENRIQUE DE MORAES
Orientador(a): Edson Takashi Matsubara
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Brasil
Palavras-chave em Português:
Link de acesso: https://repositorio.ufms.br/handle/123456789/4317
Resumo: The customer's flow count counts the number of people entering the stores. This count allows you to extract different business metrics such as conversion rate of marketing actions, time of the visit, and people's traffic. The main objective of this dissertation is to propose, develop and evaluate a solution to count customers using security cameras. The proposal is to combine deep learning algorithms for counting people. Additionally, identify people who should not be counted, such as employees and collaborators. We collected and labeled videos in two different places. We used the labeled data to train the Yolov5 to define the count by People and RetinaFace by Face. The counting performed by the proposal was compared with the manual counting using a significance test. According to the test, there was no significant difference between the system predicted and the ground truth scores. The economic feasibility presented a cost of 24.4USD per month, considering 10 hours of video per day for cloud processing. The proposed solution does not require specific hardware and modifications in the store's spaces, being a promising alternative for this problem.