Monitoring Cleanliness of Public Transportation with Computer Vision

Risto Ojala, Tuomas Kinnunen, Mikael Aakko, Joel Mattila, Panu Kiviluoma, Petri Kuosmanen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

Due to the ongoing global trend of urbanization, the demand for public transportation is ever-increasing. In order to answer to the growing demand, public transportation vehicles will likely be autonomously operated in the near future, creating a need for advanced surveillance systems. In this study, a computer vision system capable of evaluating the cleanliness of a public transportation vehicle interior is presented. In a laboratory setting mimicking a real vehicle interior, the presented system was studied with a traditional ceiling-mounted wide-angle camera, as well as a linearly actuated camera providing wider visual coverage of the environment. Image data was gathered of the setting in a clean condition as well as containing common trash objects. With this data, the evaluation accuracy of the system was measured with background subtraction, edge detection with random forest, SURF with k-means clustering and XGBoost, and Single-Shot multibox Detector. Single-Shot multibox Detector was found to be the most accurate approach, reaching 95-100% accuracy reliably. The linearly actuated camera was not found to provide notable difference in accuracy compared to the wide-angle camera. Overall, the results indicate that the presented system could be applied in cleanliness monitoring.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 5th Baltic Mechatronics Symposium
KustantajaAalto University School of Engineering
Sivumäärä6
ISBN (elektroninen)978-952-64-9603-0
TilaJulkaistu - 17 huhtik. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaBaltic Mechatronics Symposium - Espoo, Suomi
Kesto: 17 huhtik. 202017 huhtik. 2020

Conference

ConferenceBaltic Mechatronics Symposium
Maa/AlueSuomi
KaupunkiEspoo
Ajanjakso17/04/202017/04/2020

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