Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery

Open Access
Authors
Publication date 2020
Book title ICMR '20
Book subtitle proceedings of the 2020 International Conference on Multimedia Retrieval : June 08-11, 2020, Dublin, Ireland
ISBN (electronic)
  • 9781450370875
Event 10th ACM International Conference on Multimedia Retrieval, ICMR 2020
Pages (from-to) 509-516
Number of pages 8
Publisher New York, NY: The Association for Computing Machinery
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam Business School Research Institute (ABS-RI)
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
  • Faculty of Science (FNWI)
Abstract

In this paper, we propose Urban Object Detection Kit, a system for the real-time collection and analysis of street-level imagery. The system is affordable and portable and allows local government agencies to receive actionable intelligence about the objects on the streets. This system can be attached to service vehicles, such as garbage trucks, parking scanners and maintenance cars, thus allowing for large-scale deployment. This will, in turn, result in street-level imagery captured at a high collection frequency, while covering a large geographical region. Unlike more traditional panoramic street-level imagery, the data collected by this system has a higher frequency, making it suitable for the highly dynamic nature of city streets. For example, the proposed system allows for real-time detection of urban objects and potential issues that require the attention of city services. It paves the way for easy deployment and testing of multimedia information retrieval algorithms in a dynamic real-world setting. We showcase the usefulness of object detection for identifying issues in public spaces that occur within a limited time span. Finally, we make the kit, as well as the data collected using it, openly available for the research community.

Document type Conference contribution
Language English
Published at https://doi.org/10.1145/3372278.3390708
Other links https://www.scopus.com/pages/publications/85086901217
Downloads
3372278.3390708 (Final published version)
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