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Título
Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Applied Sciences
Número de la revista
21
Cita Bibliográfica
Álvarez-Aparicio, C., Guerrero-Higueras, Á. M., Calderita, L. V., Rodríguez-Lera, F. J., Matellán, V., & Fernández-Llamas, C. (2021). Convolutional neural networks refitting by bootstrapping for tracking people in a mobile robot. Applied Sciences (Switzerland), 11(21). https://doi.org/10.3390/APP112110043
Editorial
MPDI
Fecha
2021-10-27
Resumen
[EN] Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people's location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one-where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/ RTI2018-100683-B-I00/ES/DETECCION Y CARACTERIZACION AUTOMATICA DE PROBLEMAS DE CIBERSEGURIDAD EN PLATAFORMAS ROBOTICAS
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