A Novel Approach to Rail Crossing Protection Using Computer Vision and Radio Communications

• Autorzy: Rodríguez T.
• Języki publikacji: EN

Abstrakty

EN

In the paper we present an innovative computer vision based rail crossing protection system. A camera installed on top of a mast overlooking the crossing continuously monitors the scene, searching for objects that have stopped on the rail tracks. The system is designed to transmit images of the incident to the approaching trains as soon as any conflictive object has been detected. A simple user interface on board of the train displays the image sequence with a graphical aid clearly identifying the offending object in the image. In that way, train drivers are alerted of the presence of possible obstacles well before the train has approached the crossing. The system we describe operates autonomously for long periods of time without human intervention and adapts automatically to the changing environmental conditions. Several innovations, designed to deal with the above circumstances, are proposed in the paper, including: an adaptive segmentation algorithm, an innovative method for the detection of stopped objects and differentiated approaches for day and night processing.

Słowa kluczowe

EN

background substraction; computer vision; non-motion detection; object detection and tracking; rail crossing; real-time; segmentation

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2011
• Tom: Vol. 20, No. 1
• Strony: 41--71
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Rodríguez T.

• R&D Department, STI-Global, Madrid, Spain

Bibliografia

• 1. C. Tomasi and T. Kanade. Detection and tracking of point features. Number CMU-CS-91-132, April 1991.
• 2. C. Ridder, O. Munkelt, and H. Kirchner. Adaptive background estimation and foreground detection using kalman-filtering. In Proc. ICRAM 95193-195, UNESCO, 1995.
• 3. Cost effective system for railway level crossing protection. Technical Report ST/ESCAP/2088, United Nations. ESCAP. Transport Division, 2000.
• 4. Iec 61508-2 functional safety of electrical / electronic / programmable electronic safety-related systems; parts 1 to 4. Technical report, International Electrotechnical Commission, 2000.
• 5. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. Statistical and knowledge based moving object detection in traffic scenes. In Proc. of IEEE Int'l Conference on Intelligent Transportation Systems, pp. 27-32, Oct., 2000.
• 6. F. Redmill. Understanding the use, misuse and abuse of safety integrity levels. In Proceedings of tim Eighth Safety-critical Systems Symposium, Southampton, UK, pp. 226-245, February, 2000.
• 7. M. Seki, H. Fujiwara, and K. Sumi. A robust background subtraction method for changing background. In Proceedings of Fifth IEEE Workshop on Applications of Computer Vision, pp. 207-213, 2000.
• 8. N. Ohta. A statistical approach to background suppression for surveillance systems. In Proceedings of IEEE Intl. Conference on Computer Vision, pp. 481-486, 2001.
• 9. M. Watanabe, K. Okazaki, J. Fukae, and N. Tamiya. An obstacle sensing radar system for a railway crossing application: a 60 ghz millimeter wave spread spectrum radar. In Microwave Symposium Digest, IEEE MTT-S International, volume 2, pp. 791-794, August, 2002.
• 10. Qi Zang and Reinhard Klette. Object classification and tracking in video surveillance. In CAIP, pp. 198-205, 2003.
• 11. T. Rodríguez. Adaptive real-time segmentation in traffic sequences. Machine Graphics and Vision Journal, 13 (1/2) : 39-52, 2004.
• 12. J. C. Tai, S. T. Tseng, C. P. Lin, and K. T. Song. Real-time image tracking for automatic traffic monitoring and enforcement applications. 22 (6) : 485-501, June 2004.
• 13. S Turner, M. Charlwood and N. Worsell. A methodology for the assignment of safety integrity levels (SILs) to safety-related control functions implemented by safety-related electrical, electronic mad programmable electronic control systems of machines. Kluwer Academic Pub, UK Health and Safety Executive Research Report 216, 2004.
• 14. M. Ohta. Level crossing obstacle detection system using stereo cameras. RTRI Report, 46 (2) : 110-117, June, 2005.
• 15. A. D. Little. Obstacle detection at level crossings. Technical Report T522, Rail Safety and Standards Board, 2006.
• 16. Yaser Sheikh, Mubarak Shah, Bayesian Modeling of Dynamic Scenes for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 27 n. 11, pp. 1778-1792, November 2006.
• 17. Alper Yilmaz, Omar Javed, Mubarak Shah, Object tracking: A survey, ACM Computing Surveys (CSUR), v. 38 n. 4, pp. 13-es, 2006
• 18. Railway applications. systematic allocation of safety integrity requirements. Technical report, CENELEC, 2007.
• 19. E. Ota. Development of 3-d laser radar level crossing obstacle detection system. Railway and Electrical Engineering, 17 (7) : 29-32, August 2007.
• 20. Evaluation of cost-effective systems for railway level-crossing protection. Technical report, KONKAN RAILWAY CORPORATION LIMITED, 2008.
• 21. Testing protocol of the sppa rail crossing protection system. Technical report, STI-Global, 2008.
• 22. Y. Hisamitsu, K. Sekimoto, M. Nagat, M. UEHARA, and E. Ota. 3-d laser radar level crossing obstacle detection system. IHI Engineering Review, 41 (2) : 51-57, August, 2008.
• 23. Jun Xue, Jun Cheng, Li Wang, Xiaorong Gao, Visual Monitoring-Based Railway Grade Crossing Surveillance System, Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2, pp. 427-431, May 27-30, 2008.
• 24. Railway applications, communications, signalling and processing systems, application guide for en 50129. part 2: safety assurance. Technical report, CENELEC, 2009.
• 25. N. Fakhfakh, L. Khoudour, E. M. El-Koursi, J. L. Bruyelle, J. Jacot, and A. Dufaux. Background subtraction and 3d localization of moving and stationary obstacles at level crossings. In International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, 2010.
• 26. Tomás Rodríguez , Narciso García, An adaptive, real-time, traffic monitoring system, Machine Vision and Applications, v. 21 n.4, pp. 555-576, June 2010.
• 27. N. Fakhfakh, L. Khoudour, E. M. El-Koursi, J. Jacot, and A. Dufaux. A video-based object detection system for improving safety at level crossings. The Open Transportation Journal, 2011, 5, 45-59.