Multiple object tracking for robust quantitative analysis of passenger motion while boarding and alighting a metropolitan train
| dc.coverage | DOI: 10.1049/icp.2021.1468 | |
| dc.creator | Gómez Meza, José Sebastián | |
| dc.creator | Delpiano, José | |
| dc.creator | Velastin, Sergio A. | |
| dc.creator | Fernández, Rodrigo | |
| dc.creator | Awad, Sebastián Seriani | |
| dc.date | 2021 | |
| dc.date.accessioned | 2025-11-18T19:56:52Z | |
| dc.date.available | 2025-11-18T19:56:52Z | |
| dc.description | <p>To achieve significant improvements in public transport it is necessary to develop an autonomous system that locates and counts passengers in real time in scenarios with a high level of occlusion, providing tools to efficiently solve problems such as reduction and stabilization in travel times, greater fluency, better control of fleets and less congestion. A deep learning method based in transfer learning is used to accomplish this: You Only Look Once (YOLO) version 3 and Faster RCNN Inception version 2 architectures are fine tuned using PAMELA-UANDES dataset, which contains annotated images of the boarding and alighting of passengers on a subway platform from a superior perspective. The locations given by the detector are passed through a multiple object tracking system implemented based on a Markov decision process that associates subjects in consecutive frames and assigns identities considering overlaps between past detections and predicted positions using a Kalman filter.</p> | eng |
| dc.identifier | https://investigadores.uandes.cl/en/publications/91970fe2-47ca-4ea6-bbfb-c34abf78aa8a | |
| dc.identifier.uri | https://repositorio.uandes.cl/handle/uandes/60022 | |
| dc.language | eng | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | vol.2021 (2021) date: 2021-10-07 nr.1 p.231-238 | |
| dc.subject | Deep learning | |
| dc.subject | Faster R-CNN. | |
| dc.subject | Object detection | |
| dc.subject | Passenger counting | |
| dc.subject | YOLO v3 | |
| dc.title | Multiple object tracking for robust quantitative analysis of passenger motion while boarding and alighting a metropolitan train | eng |
| dc.type | Conference article | eng |
| dc.type | Artículo de la conferencia | spa |