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The Object Detection Kit project

The Object Detection Kit project, initiated by the City of Amsterdam and done in collaboration with a number of partners including the University of Amsterdam, was selected out of 46 submissions from 20 cities in 14 countries. Maarten Sukel’s work on this project is also part of his PhD thesis research at the UvA, supervised by Dr Stevan Rudinac and Prof. Marcel Worring.

GO SMART is an international network of Smart Cities, connecting and facilitating communication between government and industry. The GO SMART Award competition is held every year to stimulate innovative smart city solutions. It has become a representative verification of smart city achievements in various countries, and the influence of this award is significant.

Watch the video explaining how the Object Detection works Kit (selected for top-3 demos at the European Conference on Computer Vision, ECCV 2020) and read more about this research in an earlier interview with Maarten Sukel.

Related publications

The following publications have resulted from this research project to date:

  1. 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:https://doi.org/10.1145/3372278.3390708
  2. Maarten Sukel, Stevan Rudinac, and Marcel Worring. 2020. Detecting Urban Issues With the Object Detection Kit. In Proceedings of the 28th ACM International Conference on Multimedia (MM '20). Association for Computing Machinery, New York, NY, USA, 4518–4520. DOI:https://doi.org/10.1145/3394171.3414427
  3. 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:https://doi.org/10.1145/3343031.3350967