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

In this project, 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.

This project connects to SDG 11, Sustainable Cities and Communities.

Researchers
  • Maarten Sukel (City of Amsterdam)
  • Stevan Rudinac (University of Amsterdam)
  • Marcel Worring (University of Amsterdam)
Papers and media:
  • Maarten Sukel, Stevan Rudinac, and Marcel Worring. 2020. Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery. In Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR ’20). Association for Computing Machinery, New York, NY, USA, 509–516. DOI
  • Maarten Sukel, Stevan Rudinac, and Marcel Worring. 2019. Multimodal Classification of Urban Micro-Events. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19). Association for Computing Machinery, New York, NY, USA, 1455–1463. DOI
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