Monitoring Cleanliness of Public Transportation with Computer Vision

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 5th Baltic Mechatronics Symposium
PublisherAalto University, School of Engineering
Number of pages6
ISBN (Electronic)978-952-64-9603-0
Publication statusPublished - 17 Apr 2020
MoE publication typeA4 Article in a conference publication
EventBaltic Mechatronics Symposium - Espoo, Finland
Duration: 17 Apr 202017 Apr 2020

Conference

ConferenceBaltic Mechatronics Symposium
CountryFinland
CityEspoo
Period17/04/202017/04/2020

Keywords

  • computer vision
  • public transportation
  • intelligent transportation systems

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