# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
###### Abstract
The use of CNNs has increased in recent years because of the increasing computing capabilities. They are applied in every environment, from research to industry. Depending on the task to be solved, supervised or unsupervised learning techniques may be used. We focus on supervised learning since it is the most popular approach. The training process requires a dataset gathering input data, as well as their expected output. A fully functional network model requires a large volume of data to train it. The main issue has to do with gathering such a volume of data because a fussy labelling process is needed. Labelling is usually done manually, supervising every piece of input data and labelling their corresponding output. This process is very time-consuming. Some researchers propose to use external hardware to automatically label output data. These proposals have some constraints, on the one hand, it is very common that some \"noise\" is introduced because of the device's mean error. On the other hand, the hardware availability may be limited--for instance, if mobile transceivers are required for every people in the scene--and so, it could be impossible to label every piece of data correctly. To deal with the above issues, bootstrapping techniques can be useful. Bootstrapping was first proposed in [12] applied to word-sense disambiguation using unlabeled samples and a few labelled samples. An initial classifying model was built usingthe labelled samples. Then the unlabeled samples were classified extracting new patterns that were used to build an enhanced classifier. Bootstrapping has been applied in different research areas, for instance, in document analysis and recognition. The authors in [13] used bootstrapping to resolve segmentation problems in the processing of music scores getting a 99.2% classification accuracy. The authors in [14] proposed a scene text detection technique using bootstrapping and text border semantics for accurate localization of texts in scenes, getting an 80.1% f-score for the MSRA-TD500 dataset. In healthcare, the authors in [15] point out issues related to the segmentation of craniofacial cartilage images. Labelling such images is very challenging since only experts can differentiate cartilages. The authors proposed to use self-training to fit a CNN and therefore achieve high segmentation accuracy. The authors in [16] present a new prediction approach for imbalanced DNA-protein binding data, they use a bootstrap strategy to under-sample the negative data to balance the number of binding and non-binding samples. Results demonstrate that the method achieves a high prediction performance and outperforms the state-of-the-art sequence-based DNA-protein binding predictors. In agricultural engineering, the authors in [17] apply bootstrapping methods to refit a CNN which allows for segmenting plant sections. The proposed CNN was initially fitted with only 30 images manually labelled. In industry, the study presented in [18] uses bootstrapping to predict the useful life of a rolling bearing. The performance increased significantly by testing through different datasets and compared to MSCNN-based, BLSTM-based, and MLP-based models. In the robotics field, bootstrapping techniques have been applied in some research. The authors in [19] propose a novel pipeline for object detection using bootstrapping that improves 60-fold the training speed. They assess the effectiveness of the approach on a standard Computer Vision dataset (PASCAL VOC 2007 [20]) and demonstrate its applicability to a real robotic scenario with the iCubWorld Transformations [21] dataset. The authors of [22] use bootstrapping to create a method of teaching a [PERSON]'s robot its empathic behavioural response from its interaction with people. The results show that this technique is an efficient tool to speed up the robot's learning compared to the online learning method initially used. In this work, we propose to use bootstrapping for refitting PeTra.