People detection and tracking using deep learning based approaches Abrego-González, José Aguirre Molina, Eugenio García Silvente, Miguel People detection and tracking Deep learning YOLO TensorFlow This work was made possible thanks to the support of Senacyt Panamá (Scholarship No. 270-2022-164) and Grant PID2022-138453OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe.” 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. 2024-09-13T10:35:31Z 2024-09-13T10:35:31Z 2024 conference output Proceedings of the 23rd International Workshop of Physical Agents (WAF 2023). Pág. 52-66. https://hdl.handle.net/10481/94417 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional