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Transport Problems

Silesian University of Technology

Subject: Economics, Transportation, Transportation Science & Technology


eISSN: 2300-861X



VOLUME 15 , ISSUE 4, Part 1 (December 2020) > List of articles



Keywords : infrared sensors; linearization algorithm; collision detection; blind data algorithms; tracking objects; Intel R200

Citation Information : Transport Problems. Volume 15, Issue 4, Part 1, Pages 105-116, DOI:

License : (CC BY 4.0)

Received Date : 12-June-2019 / Accepted: 27-December-2020 / Published Online: 31-December-2020



In this investigation, the problem of moving object detection - without any knowledge - is classified. It describes a technique that will allow real-time localization with usage of IR sensors. The proposed algorithm is simplistic, and in the future, it might be implemented into any vehicle, premium or entry level. It is guided by AI that must calculate its next moves in the blink of an eye without user noticing any delays. The main problem of moving object recognition was extraction of proper features, description of the events, and choice of only the crucial ones. The presented novel approach does not follow any standard algorithms. It is a practical hardware implementation of custom solution, based on processing system, which can be well situated in the safety modules of future cars.

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1. Polish statistics about traffic collisions 2016. Chapter 2, Data about cars and collisions, Chapter 3, Time and place of collisions. ITS. Warsaw, 2016. P. 5-21. Available at:

2. Automotive emergency braking initiative, NHTSA-2015-0101. U.S. Department of Transportation. National Highway Traffic Safety Administration. March 2016.

3. Chen, Y. & Zhang, Rh. & Shang, L. A Novel Method of Object Detection from a Moving Camera Based on Image Matching and Frame Coupling. In: PLoS ONE. 2014. Vol.9. No.10. P. 3-5.

4. Shengping, Z. & Hongxun, Y & Shaohui, L. Spatial-temporal nonparametric background subtraction in dynamic scenes. In: 2009 IEEE International Conference on Multimedia and Expo, ICME 2009. New York City. 2009. P. 2-3.

5. Wei, Z. & Xiangzhong, F. & Xiaokang, Y, & Wu, J. Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes. Journal of Electronic Imaging. May 2007. Vol. 16. No. 2. P. 2-4.

6. Royden, C & Holloway, M. Detecting moving objects in an optic flow field using direction- and speed-tuned operators. Vision Research. May 2014. Vol. 98. P. 14-25.

7. Royden, C. & Sannicandro, S. & Webber, L. Detection of moving objects using motion- and stereo-tuned operators. Journal of Vision. June 2015. Vol. 15. No. 8. P. 3-4.

8. Rodríguez-Canosa, G. & Thomas, S & Cerro, J. & Barrientos, A. & MacDonald, B. Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera. Remote Sensing. April 2012. Vol. 4. No. 4. P. 9-12.

9. Araki, S & Matsuoka, T. & Takemura, H. & Yokoya, N. Real-time tracking of multiple moving objects in moving camera image sequences using robust statistics. In: Proceedings. Fourteenth International Conference on Pattern Recognition. 1998. P. 3-4.

10. Kim, D. & Kwon, J. Moving Object Detection on a Vehicle Mounted Back-Up Camera. Sensors. December 2015. Vol. 16. No. 1. Switzerland. P. 4-8.

11. Talukder, A & Goldberg, S. Matthies, L. Ansar, A. Real-time detection of moving objects in a dynamic scene from moving robotic vehicles. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2003. Las Vegas. October 2003. P. 3-5.

12. Lefaix, G. & Marchand, E. & Bouthemy, P. Motion-based obstacle detection and tracking for car driving assistance. In: IAPR Int. Conf. on Pattern Recognition. ICPR'02. Quebec, 2002. P. 74-77.

13. Chen, Y. & Zhang, Rh. & Shang, L. & Hu, E. Object detection and tracking with active camera on motion vectors of feature points and particle filter, Review of Scientific Instruments. June 2013. Vol.84. No. 6. P. 3-4.

14. Junghye, M. & Kasturi, R. Activity recognition based on multiple motion trajectories. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. September 2004. Vol. 4. Cambridge. P. 3.

15. U.S. DOT and IIHS announce historic commitment of 20 automakers to make automatic emergency braking standard on new vehicles, U.S. Department of Transportation. National Highway Traffic Safety Administration. Washington, DC. March 17, 2016. Available at:

16. Jiménez, F. & Naranjo, J. & García, F. An Improved Method to Calculate the Time-to-Collision of Two Vehicles, International Journal of Intelligent Transportation Systems Research. January 2013. Vol. 11. No. 1. P. 34-42.

17. Polish statistics about traffic collisions 2015, Chapter 2, Data about cars and collisions, Chapter 3, Time and place of collisions, ITS. Warsaw. 2015. P. 5-20. Available at:

18. Mrozik, M. Tests for speed evaluation of pedestrian traffic users move. Advances in Science and Technology Research Journal. 2010. Vol. 5. P. 138-144. Lublin.

19. Prędkości pojazdów w Polsce Raport 2013, Krajowa Rada Bezpieczeństwa Ruchu Drogowego, Gdańsk, Kraków, Warszawa, Poland. 2014. P. 13-18. Available at: [In Polish: 2013 Report of vehicle speeds in Poland, National Road Safety Council].

20. 2013 Production Statistics, Int. Organization of Motor Vehicle Manufacturers. OICA. Paris, France. 2013. Available at:

21. Opis i rekonstrukcja wypadków drogowych, Faculty of automotive and construction machinery engineering, Warsaw University of Technology. Warszawa. 2016, P. 117-133. Available at: [In Polish: Reconstruction of traffic collisions].

22. Blais F. Review of 20 years of range sensor development. Journal of Electronic Imaging. January 2004. Vol. 13. No. 1. P. 3-4.

23. Integrating Safety into the EU’s Urban Transport Policy. European Transport Safety Council. ETSC. Brussels, Belgium. June 2013. P. 1-10. Available at:

24. European Road Safety Report 2014 Urban Mobility, Strategies to prevent accidents on Europe’s roads. DEKRA Automobil. GmbH. 2014. P.6-31. Available at:

25. Szablata, P. n& Łąkowski, P. & Pochmara, J. Processing depth distance data to increase precision of multiple infrared sensors in the automatic visual inspection system. Optical Engineerin. August 2017. Vol. 56. No. 8. P. 3-6.

26. Mankoff, K & Russo, T. The Kinect: a low-cost, high-resolution, short-range 3D camera. Earth Surface Processes and Landforms. November 2012. Vol. 38. No. 9. P. 926-936. John Wiley & Sons Ltd.

27. Intel® RealSense™ Camera R200, Embedded Infrared Assisted Stereovision 3D Imaging System with Color Camera, Rev. 001. Intel Corporation. June 2016. P. 15-19. Available at:

28. Gollapudi, S. Machine learning vs Deep learning vs Artificial Intelligence. 24 July 2017. Available at: