1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.028

Simulation of Computer Vision Based Sensor System for Autonomous Transport

  • a Mikhail Gorobetz,
  • b Ambuja Bangalore Srinivasa 
  • a,b  Riga Technical University, Kal,k, u iela 1, Centra rajons, Rıga, LV-1658, Latvia
Cite as
Gorobetz M., Bangalore Srinivasa A. (2021). Simulation of Computer Vision Based Sensor System for Autonomous Transport. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 208-214. DOI: https://doi.org/10.46354/i3m.2021.emss.028

Abstract

The paper provides a new insight into perception problem of autonomous transport. This study is dedicated to solve sensor fusion problem in intelligent transport system by performing tasks of object detection, real-time recognition and also determine motion parameters using solo camera as sensor. Study deals with the simulation model for a computer vision based sensor which can be very economical. A simulation model of novel system structure for recognition, speed estimation process using solo camera sensor and CNN algorithm with training is proposed in this paper. The entire computational complexity is supposed to be significantly lower in comparison with actual experimentation. From this point of view, the simulation of sensor system can be used for optimization and better results of object detection and recognition.The computer model and simulation results along with the workability of the system is presented in the paper.

References

  1. Chollet, F. (2018). 16-Keras. Deep Learning with Python, pages 97–111. 
  2. Do, T.-h., Tran, D.-k., Hoang, D.-q., Vuong, D., and Hoang, T.-m. (2021). A Novel Algorithm for Es timating Fast-Moving Vehicle Speed in Intelligent Transport Systems. pages 499–503. 
  3. El Bouziady, A., Oulad Haj Thami, R., Ghogho, M., Bourja, O., and El Fkihi, S. (2018). Vehicle speed es timation using extracted SURF features from stereo images. 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, 2018-May:1– 6. 
  4. Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter:1440–1448. 
  5. Gorobetz, M., Alps, I., Beinarovica, A., and Levchenkov, A. (2018). Algorithm of signal recognition for railway embedded control devices. 2018 IEEE 59th Annual In ternational Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2018 - Proceedings, pages 7–11. 
  6. Han, S., Yoo, J., and Kwon, S. (2019). Real-time vehicle detection method in bird-view unmanned-aerial vehicle imagery. Sensors (Switzerland), 19(18):1–17. 
  7. Oltean, G. and Oltean, V. (2019). Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation. (October):2019–2022. 
  8. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once : Unified , Real-Time Object Detection.