Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

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 labe...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2021-11, Vol.11 (21), p.10043
Main Authors: Álvarez-Aparicio, Claudia, Guerrero-Higueras, Ángel Manuel, Calderita, Luis V., Rodríguez-Lera, Francisco J., Matellán, Vicente, Fernández-Llamas, Camino
Format: Article
Language:English
Subjects:
Accuracy
bootstrapping
convolutional neural networks
Datasets
Distance learning
Labeling
LIDAR
Neural networks
Performance evaluation
PeTra
re-training
robotics
Robots
Sensors
Skills
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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%.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112110043