# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
## 5 Conclusions  
This paper presents a comparative study of fitting a CNN with data labelled by bootstrapping versus data labelled with an external ground-truth system. Specifically, this work compares the performance of the CNN used by the PeTra tool to track people close to a mobile service robot. CNN performance was evaluated with a new dataset gathered for such purpose. The CNN has been fitted twice: the first time by using training data labelled with the KIO RTLS device, and the second time by using the PeTra tool itself to label the training data. We analysed the KPIs of both models. Results show that labelling with PeTra improves four out of the five KPIs described. Sensitivity in the CNN trained with bootstrapping increases by 54.16% compared with CNN trained by KIO. The Specificity is 72.43% higher in the CNN trained with by bootstrapping compared to the CNN trained with KIO. Furthermore, the Accuracy of the CNN trained with bootstrapping is 35.71% higher than the CNN trained with KIO. Finally, the MCC KPI is 65.97% higher in the CNN trained by bootstrapping compared to the CNN trained with KIO. Thus, this work allows to assert that bootstrapping increases the accuracy of a CNN-based people tracking tool. The improvement is significant in the cases where there are no people in the scene (Rosbags 8-25). In such cases, the CNN trained by bootstrapping does not detect any people where there are none. Therefore, using bootstrapping to label data is a good alternative to get a more accurate CNN. Moreover, this method is an especially interesting alternative in environments where a RTLS or a ground-truth system is not available. Moreover, it is important to point out that PeTra allows to label data of all people in the scene, whereas the KIO device--as the most commercial RTLS--has a limited number of mobile transceivers. In addition, PeTra's CNN refitted by bootstrapping performs especially well in scenarios without people, thus avoiding false positives and any possible incorrect robot behaviour. Similarly, in scenarios where there are a larger number of people, it also improves significantly. Furthermore, our solution provides a labelling method suitable to use in scenarios where a ground-truth system is not available. The source code of PeTra is available online under an open-source license [35]. A docker image with all required software to test PeTra and to double-check the overall evaluation posed in this paper is also available online under an open-source license [36]. Conceptualization, C.A.-A. and A.M.G.-H.; methodology, A.M.G.-H. and V.M. ; software, C.A.-A. ; validation, C.A.-A., A.M.G.-H. and L.V.C. ; formal analysis, F.J.R.-L. and V.M. ; investigation, C.A.-A., A.M.G.-H., L.V.C., F.J.R.-L. and V.M. ; resources, V.M. ; data curation, C.A.-A. ; writing--original draft preparation, C.A.-A. and A.M.G.-H.; writing--review and editing, L.V.C., F.J.R.-L., V.M. and C.F.-L.; visualization, C.A.-A. and A.M.G.-H.; supervision, V.M. ; project administration, C.F.-L.; funding acquisition, C.F.-L. All authors have read and agreed to the published version of the manuscript. The research described in this article has been partially funded by the grant RTI2018-100683-B-I00 funded by MCIN/AEI/ 10.1309/501100011033 and by \"ERDF A way of making Europe\"; Instituto Nacional de Ciberseguidad de Espa(INCIBE), under the grant \"ADENDA 4: Deteccion de nuevas amenazas y patrones desconocidos (Red Regional de Ciencia y Tecnologia)\", addendum to the framework agreement INCIBE-Universidad de Leon, 2019-2021; and the regional Government of Castilla y Leon under the grant BDNS (487971). Ethical review and approval were waived for this study, due to the fact that subjects involved in the experimentation can not be identified directly or indirectly with the data collected for this work. No personal data was recorded according to GDPR Art. 4.1. **Informed Consent Statement:** Not applicable.