Detecting and Tracking Regions of Interest for Remote Measurement of Vital Parameters
| dc.contributor.author | Müller, Madeleine | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Physics | en |
| dc.contributor.examiner | Forssen, Christian | |
| dc.contributor.supervisor | Garcia Lozano, Marianela | |
| dc.date.accessioned | 2022-11-07T08:59:32Z | |
| dc.date.available | 2022-11-07T08:59:32Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2020 | |
| dc.description.abstract | The initial assessment of a mass casualty incident is essential to effectively conduct a rescue operation. The survival rate is affected by the complexity of the incident, and it is therefore imperative to enhance the operational capacities of emergency medical services and civil protection agencies in mass casualty incidents. This thesis investigates the possibilities for an unmanned aerial vehicle (UAV) to detect and track regions of interest for remote measurement of vital parameters in visual and thermal footage for first response triage purposes. The regions of interest are the nose, mouth, and chest, and the UAV characteristic taken under consideration in this thesis is image blur due to random camera motion. In this thesis, we take an object detection approach and implement the keypoint estimation framework KAPAO and the tracking algorithm SORT in several different experimental setups. Using KAPAO and SORT, we achieve a good result. For the detection in the thermal domain, the model created by transferring knowledge from the visual to the thermal domain achieves the highest performance. We also consider adversarial training on random motion blur, however the result shows a minimal impact on the model performance in the presence of characteristic low-altitude UAV motion blur. Regarding the tracking of the regions of interest, the result concludes that the SORT algorithm improves the performance compared to assigning tracking identification numbers based on frame-to-frame differences. The result shows that the distance to the subjects and the image quality impacts the performance. Compared with previous work on remote measurement of vital parameters, the algorithms of this thesis achieve a nearly perfect score on corresponding distances. If the distances are realizable in a UAV triage application is however unknown and has to be investigated further. Moreover, the work of this thesis problematizes the low-altitude UAV motion blur which poses a potential limitation in a potential UAV triage application. An alternative could hence be to use optical stabilization measurement for blur reduction. | |
| dc.identifier.coursecode | TIFX05 | |
| dc.identifier.uri | https://odr.chalmers.se/handle/20.500.12380/305795 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.title | Detecting and Tracking Regions of Interest for Remote Measurement of Vital Parameters | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Physics (MPPHS), MSc |