@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}, }