Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90507
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Dixe, Sandra | por |
dc.contributor.author | Sousa, Joao | por |
dc.contributor.author | Fonseca, Jaime C. | por |
dc.contributor.author | Moreira, António Herculano Jesus | por |
dc.contributor.author | Borges, Joao | por |
dc.date.accessioned | 2024-04-03T11:48:12Z | - |
dc.date.available | 2024-04-03T11:48:12Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Dixe, S., Sousa, J., Fonseca, J. C., Moreira, A. H. J., & Borges, J. (2022). Optimized in-vehicle multi person human body pose detection. Procedia Computer Science. Elsevier BV. http://doi.org/10.1016/j.procs.2022.08.059 | por |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | https://hdl.handle.net/1822/90507 | - |
dc.description.abstract | The number of Shared Autonomous Vehicles (SAV) will increase in the coming years. The absence of human driver will create a new paradigm for in-car safety. This paper addresses this problem by presenting an approach to estimate the human body pose inside a vehicle. We propose to use a customized version of the OpenPose framework, to perform the task of human body pose detection for the front passengers inside a vehicle. The OpenPose method was been evaluated with three different backbones: VGG19, MobileNetV1 and MobileNetV2, using different hyperparameters and ablation scenarios. Moreover, synthetic images were used, which simulate a depth sensor perspective from the center of the front seats. The dataset is comprised by images with 1 and 2 passengers, from 18 different subjects inside of 7 different vehicles, thus making a total of 45360 different images. The OpenPose method with the MobileNetV2 backbone showed the most efficient results between precision and performance, achieving a mean Average Precision (mAP) of 90%, Area Under ROC Curve (AUC) of 73%, and 0.0189 seconds per image (s/img). | por |
dc.description.sponsorship | - (undefined) | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | por |
dc.subject | Body Posture Detection | por |
dc.subject | Computer Vision | por |
dc.subject | Deep Learning | por |
dc.subject | Shared Autonomous Vehicles | por |
dc.title | Optimized in-vehicle multi person human body pose detection | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1877050922007979 | por |
oaire.citationStartPage | 479 | por |
oaire.citationEndPage | 487 | por |
oaire.citationVolume | 204 | por |
dc.date.updated | 2024-04-03T11:26:42Z | - |
dc.identifier.doi | 10.1016/j.procs.2022.08.059 | por |
sdum.export.identifier | 16013 | - |
sdum.journal | Procedia Computer Science | por |
sdum.conferencePublication | Procedia Computer Science | por |
oaire.version | VoR | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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optimized in-vehicle.pdf | 962,75 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons