# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
###### 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%. bootstrapping; convolutional neural networks; LIDAR; PeTra; re-training; robotics +
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and Ranging (LIDAR) sensors and cameras. In [5], the authors propose to combine 2D and 3D LIDAR data to train a Support Vector Machine (SVM) to detect pedestrians for autopilot systems. However, the above approaches are both computing-demanding when running on-board a robot. Different solutions have been proposed in the literature to deal with the problem of tracking people using 2D LIDAR sensors despite their being traditionally used for autonomous navigation. Different methods for robot navigation in crowded indoor environments have been reviewed in [6]. The usage of the geometric features of the human legs, as well as the rate and phase of gait, was proposed in [7] to enhance people tracking systems. Furthermore, in [8], the clustering and centre point estimation combined with the walking centre line estimation, the speed, and the step length separation are used to detect people with or without a walker. However, these approaches are not robust enough when dealing with occlusions or changes in gait speed. To address such issues, we presented People Tracking (PeTra) in [9], a tool that allows to locate people within the robot surroundings using the information provided by a LIDAR and a CNN. PeTra is used in this work to validate our proposal. The first release was presented in [10]. The system builds an occupancy map through a LIDAR sensor's readings. The sensor is located 20 cm above the floor. LIDAR's readings are processed by a CNN which returns a second occupancy map segmenting the readings belonging to people close to the robot. From the second occupancy map, a centre-of-mass calculation provides the people's location estimates. A new PeTra release was presented in [11] including not only a correlation method of location estimates for tracking people in time but also an optimized model for the CNN which allows the system to work in real time. PeTra provides a good performance in the scenarios where it has been evaluated. However, its performance is worse in some specific locations, such as empty rooms without furniture; corridors; or scenarios with more than two people. In such cases, PeTra sometimes detects people where there are none, for instance, close to the walls. Thus, it may be convenient to refit the PeTra's CNN to achieve better performance in such environments. To obtain a supervised learning model with high accuracy, it is necessary to provide a large volume of data at the training phase. Generally, CNNs have been fitted by using manually labelled data. The labelling process is very time-consuming. Some researchers automatically label data by using external hardware. External hardware may report positive results but it also introduces some constraints related to the number of required devices or their measurement error. Despite the above issues, a CNNs fitted with such labelled data usually provides acceptable results. A popular alternative is using bootstrapping techniques, which is somehow similar to self-training.