@misc{10481/94417,
year = {2024},
url = {https://hdl.handle.net/10481/94417},
abstract = {The primary objective of this work is to develop an efficient
and rapid proposal for detecting and tracking persons based on images
captured by a mobile robot. This will be achieved through the integration
of deep learning-based detectors and state-of-the-art tracking algorithms.
Pre-trained detectors on the COCO dataset will be evaluated to identify
the most effective ones for human detection, and tracking capabilities
will subsequently be added through cutting-edge algorithms. The effec-
tiveness of the solution will be measured using specialized datasets and
specific performance metrics. Additionally, a new dataset, ROBOT_7,
will be created, designed to reflect the operational scenarios of the mo-
bile robot PeopleBot. An extensive experimentation has been carried
out in order to identify the best combination of detector and tracking
algorithm for this application. As conclusion, we propose a specific com-
bination of detector and tracking algorithm that achieves high levels of
F1-score and CLEAR MOT performance achieving rates of frames per
second good enough for a real-time performance.},
organization = {Senacyt Panamá (Scholarship No. 270-2022-164)},
organization = {MCIN/AEI/10.13039/501100011033  PID2022-138453OB-I00},
organization = {“ERDF A way of making Europe”},
keywords = {People detection and tracking},
keywords = {Deep learning},
keywords = {YOLO},
keywords = {TensorFlow},
title = {People detection and tracking using deep learning based approaches},
author = {Abrego-González, José and Aguirre Molina, Eugenio and García Silvente, Miguel},
}