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
References:
- [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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- L. Bartusˇka, V. Biba, and R. Kampf, “Modeling of daily traffic volumes on urban roads,” 2016.
- Metropolitan Washington Council of Govern- ments. Traffic Counts - Hourly Classification Counts 2017. Accessed Mar. 20, 2021. [Online]. Available: https://rtdc-mwcog.opendata.arcgis.com/ datasets/fae4f4ebf99c45088adbfba504efd650
- Bike Counts (Eco Counter). City of Edmonton. Accessed Mar. 20, 2021. [Online]. Available: https: //data.edmonton.ca/Monitoring-and-Data-Collection/ Bike-Counts-Eco-Counter-/tq23-qn4m
- City of Melbourne Open Data Team. Pedes- trian Counting System - Monthly (counts per hour). Accessed Mar. 20, 2021. [Online]. Avail- able: https://data.melbourne.vic.gov.au/Transport/ Pedestrian-Counting-System-Monthly-counts-per-hour/ b2ak- trbp
- S. Buchmueller and U. Weidmann, “Parameters of pedestrians, pedestrian traffic and walking facilities,” IVT Schriftenreihe, vol. 132, 2006.
- E. Brolin, “Anthropometric diversity and considera- tion of human capabilities,” p. 101.
- N. HajiGhassemi and M. Deisenroth, “Analytic long- term forecasting with periodic gaussian processes,” in Artificial Intelligence and Statistics. pp. 303–311.
|
HOME
- Call for Papers
- Paper Submission
- For authors
- Important Dates
- Conference Committee
- Editorial Board
- Reviewers
- Last Proceedings
PROCEEDINGS
-
Volume 12, Issue 1 (ICAIIT 2024)
-
Volume 11, Issue 2 (ICAIIT 2023)
-
Volume 11, Issue 1 (ICAIIT 2023)
-
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)
PAST CONFERENCES
ICAIIT 2024
-
Photos
-
Reports
ICAIIT 2023
-
Photos
-
Reports
ICAIIT 2021
-
Photos
-
Reports
ICAIIT 2020
-
Photos
-
Reports
ICAIIT 2019
-
Photos
-
Reports
ICAIIT 2018
-
Photos
-
Reports
ETHICS IN PUBLICATIONS
ACCOMODATION
CONTACT US
|
|