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Multirole UAVs Supported Parking Surveillance System

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Abstract

The paper deals with the possible application of Unmanned Aerial Vehicles (UAVs) in recognizing irregular traffic situations. This primarily refers to improperly parked vehicles. Fleets of drones are used, and their roles can be observers or inspectors, where each of the roles has specially defined tasks. A case study was conducted in a real urban environment. Traveling salesman problem and Vehicle Routing Problem algorithms were used to determine drone trajectories. The study assumes difficulties in collecting a sufficient number of images for successful training of Convolutional Neural Network, so a synthesized training set was used. This process assumes the use of object detection algorithms. Accuracy was tested on a small test set of real-life images. It has been shown that by moving some of the real-life images from the test set to the training set, the accuracy can be increased. In addition, the paper describes the architecture of the Smart UAV Monitoring System for Parking Supervision (SmartUAV-PS), and tests its functioning in real conditions.

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Authors and Affiliations

  1. University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Bosnia and Herzegovina

    Goran Jausevac & Gordana Jotanovic

  2. University of Novi Sad, Technical faculty “Mihajlo Pupin” Zrenjanin, Novi Sad, Serbia

    Dalibor Dobrilovic, Vladimir Brtka & Zeljko Stojanov

  3. University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, Croatia

    Dragan Perakovic

Authors
  1. Goran Jausevac
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  2. Dalibor Dobrilovic
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  3. Vladimir Brtka
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  4. Gordana Jotanovic
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  5. Dragan Perakovic
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  6. Zeljko Stojanov
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Corresponding author

Correspondence to Goran Jausevac.

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“This paper is an extended version of my previously published scientific work with the title: “Smart UAV Monitoring system for Parking Supervision” on the conference EAI FABULOUS 2021.“

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Jausevac, G., Dobrilovic, D., Brtka, V. et al. Multirole UAVs Supported Parking Surveillance System. Mobile Netw Appl (2022). https://doi.org/10.1007/s11036-022-02045-y

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  • DOI: https://doi.org/10.1007/s11036-022-02045-y

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