Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
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Published:2021-10-27
Issue:21
Volume:11
Page:10043
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Álvarez-Aparicio ClaudiaORCID,
Guerrero-Higueras Ángel ManuelORCID,
Calderita Luis V.ORCID,
Rodríguez-Lera Francisco J.ORCID,
Matellán VicenteORCID,
Fernández-Llamas CaminoORCID
Abstract
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%.
Funder
Spanish Ministry of Science, Innovation and Universities
Junta de Castilla y León
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
1 articles.
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