Proceedings of International Conference on Applied Innovation in IT
2021/04/28, Volume 9, Issue 1, pp.61-67

Urban Environment Simulator for Train Data Generation Toward CV Object RecognitionModel of Damage Accumulation in Predicting the Technical Condition of a Fiber-Optic Cable

Kirill Karpov, Ivan Luzianin, Maksim Iushchenko, Eduard Siemens

Abstract: Detecting moving objects in an urban environment is a challenging and widely explored problem in computer vision. This task requires huge amounts of data. Their obtaining and labeling is challenging. However the available datasets are not always fit the task. This work proposes a framework for synthesizing the train data based on 3D visualization of an urban environment using Unity 3D. Methods of mathematical statistics and distribution theory were used to build the background models of the framework. The framework, presented in the article, allows to simulate the real urban environment in an adjustable 3D virtual scene. It considers differ- ent environmental parameters amd makes possible to simulate the real behavior and physical characteristics of moving objects.

Keywords: Virtual Reality, Computer Vision, Object Detection, Public Street Environment, Simulation, Traffic, Pedes- trian, Distribution, Labeling

DOI: 10.25673/36585

Download: PDF


  1. [I.Matveev,K.Karpov,A.Yurchenko,andE.Siemens, “The object tracking algorithm using dimensional based detection for public street environment,”Eurasian Physical Technical Journal, vol. 17, pp. 123– 127, Dec. 2020.
  2. I. Matveev, K. Karpov, I. Chmielewski, E. Siemens, and A. Yurchenko, “Fast object detection using di- mensional based features for public street environ- ments,” Smart Cities, vol. 3, no. 1, pp. 93–111, 2020.
  3. X.Ouyang,Y.Cheng,Y.Jiang,C.-L.Li,andP.Zhou, “Pedestrian-synthesis-gan: Generating pedestrian data in real scene and beyond,” arXiv preprint arXiv:1804.02047, 2018.
  4. W. Zhang, K. Wang, H. Qu, J. Zhao, and F.-Y. Wang, “Scene-specific pedestrian detection based on parallel vision,” arXiv preprint arXiv:1712.08745, 2017.
  5. H. Hattori, V. N. Boddeti, K. Kitani, and T. Kanade, “Learning scene-specific pedestrian detectors without real data,” in 2015 IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, Jun. 2015, pp. 3819–3827.
  6. J. Nilsson, P. Andersson, I. Y. Gu, and J. Fredriks- son, “Pedestrian detection using augmented training data,” in 2014 22nd International Conference on Pat- tern Recognition. IEEE, 2014, pp. 4548–4553.
  7. F. Moretti, S. Pizzuti, S. Panzieri, and M. Annunziato, “Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling,” Neurocomputing, vol. 167, pp. 3–7, 2015.
  8. L. Bartusˇka, V. Biba, and R. Kampf, “Modeling of daily traffic volumes on urban roads,” 2016.
  9. Metropolitan Washington Council of Govern- ments. Traffic Counts - Hourly Classification Counts 2017. Accessed Mar. 20, 2021. [Online]. Available: datasets/fae4f4ebf99c45088adbfba504efd650
  10. Bike Counts (Eco Counter). City of Edmonton. Accessed Mar. 20, 2021. [Online]. Available: https: // Bike-Counts-Eco-Counter-/tq23-qn4m
  11. City of Melbourne Open Data Team. Pedes- trian Counting System - Monthly (counts per hour). Accessed Mar. 20, 2021. [Online]. Avail- able: Pedestrian-Counting-System-Monthly-counts-per-hour/ b2ak- trbp
  12. S. Buchmueller and U. Weidmann, “Parameters of pedestrians, pedestrian traffic and walking facilities,” IVT Schriftenreihe, vol. 132, 2006.
  13. E. Brolin, “Anthropometric diversity and considera- tion of human capabilities,” p. 101.
  14. N. HajiGhassemi and M. Deisenroth, “Analytic long- term forecasting with periodic gaussian processes,” in Artificial Intelligence and Statistics. pp. 303–311.



       - Call for Papers
       - Paper Submission
       - Important Dates
       - Committees
       - Guest registration
       - Proceedings


       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports





           ISSN 2199-8876
           Copyright © 2013-2021 Leonid Mylnikov, © 2022 at Anhalt University of Applied Sciences. All rights reserved.