Human detection on antistatic floors

• Autores: Paiva Peredo, Ernesto Alonso, Vaghi, Alessandro, Montú, Gianluca, Bucher, Roberto
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/7807
• Enlace del recurso: https://hdl.handle.net/20.500.12867/7807; https://doi.org/10.1016/j.iswa.2023.200254
• Nivel de acceso: acceso abierto
• Materia: Deep learning; Long-short term memory; Electric discharges; Human detection; https://purl.org/pe-repo/ocde/ford#1.02.01

Nowadays, a correct detection of people is very important for different purposes. Most applications use images as sources of information. However, an image may contain more information than is necessary for the detection task. For this reason, raw video images can end up being used for malicious purposes or privacy concerns. We present baseline results for a new human detection task. We evaluate long short-term memory (LSTM)-based deep learning models for detecting people using electrical signals from electrostatic discharge (ESD) floors as a source of information. Statistical features were provided to the models every second and four classification problems were studied. The first model discriminates between motion and non-motion. A second model classifies the action of the person between: no person, walking or standing. A third model classifies between walking and standing. And a last model predicts whether there is someone or no one on the ESD floor. Mattews Correlation Coefficient (MCC) was used as the main metric to evaluate the performance of the models. The LSTM models obtained a MCC between 0.94 and 0.99.