한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

  • 제39C권3호
  • /
  • Pages.255-263
  • /
  • 2014
  • /
  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지
DOI QR코드

DOI QR Code

칼로게로 모제 시스템을 활용한 4차선 도로의 사고검지 폐쇄회로 카메라 시스템

CCTV-Aided Accident Detection System on Four Lane Highway with Calogero-Moser System

  • 투고 : 2013.11.01
  • 심사 : 2014.03.07
  • 발행 : 2014.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

도로변에 설치된 폐쇄회로 카메라를 통해 사고를 감지하여 교통사고 대책반에 전송하는 시스템이 연구되어 많은 성과를 거두고 있다. 더하여 고속도로에서는 고장으로 인한 정지차량이 차량의 흐름을 방해하는 것도 사고로 간주해야 하는 상황이 발생한다. 본 논문에서는 차량의 흐름을 각 차선 별로 모니터링하고 있다가 정지차량이나 사고로 인한 차량흐름의 변화를 감지하여 이를 사고 대책반에 알리는 시스템을 소개한다. 각 차선 별 차량흐름은 레벨 스페이싱 곡선들로서 위치벡터에 대한 Wigner 분포를 이룬다. 여기에 해밀토니안 및 칼로게로 모제 시스템을 적용하면 각 레벨스페이싱 커브간의 간격에 대한 확률식을 얻게된다. 이 식으로부터 변동이 큰 이상 신호를 찾으면 사고 상황과 잘 맞는다. 이것은 한 차선에 대한 이상 신호를 찾는 것과는 다르다. 전체적인 차량 흐름 속에서 찾아야만 사고를 감지하는 효과를 보기 때문이다. 각 차선 별 차량흐름을 모니터링 하는 과정에서 카메라의 특성상 차량의 그림자를 차량으로 오인하게 되면 사고감지에도 영향을 미친다. 이를 방지하기 위해 그림자를 제거하는 방법도 소개한다. 본 시스템의 평가를 위해 베이지안 네트워크 방법을 사용한 시스템과 비교하였다. 특별히 고장으로 인한 정지차량으로 생겨난 차량흐름의 변화를 사고로 인식하는 데는 본 시스템이 우수한 것으로 나타났다.

Today, a number of CCTV on the highway is to observe the flow of traffics. There have been a number of studies where traffic data (e.g., the speed of vehicles and the amount of traffic on the road) are transferred back to the centralized server so that an appropriate action can be taken. This paper introduces a system that detects the changes of traffic flows caused by an accident or unexpected stopping (i.e., vehicle remains idle) by monitoring each lane separately. The traffic flows of each lane are level spacing curve that shows Wigner distribution for location vector. Applying calogero-moser system and Hamiltonian system, probability equation for each level-spacing curve is derived. The high level of modification of the signal means that the lane is in accident situation. This is different from previous studies in that it does more than looking for the signal from only one lane, now it is able to detect an accident in entire flow of traffic. In process of monitoring traffic flow of each lane, when camera recognizes a shadow of vehicle as a vehicle, it will affect the accident detecting capability. To prevent this from happening, the study introduces how to get rid of such shadow. The system using Basian network method is being compared for capability evaluation of the system of the study. As a result, the system of the study appeared to be better in performance in detecting the modification of traffic flow caused by idle vehicle.

키워드

참고문헌

  1. V. Goud and V. Padmaja, "Vehicle accident automatic detection and remote alarm device," Int. J. Reconfigurable Embedded Syst. (IJRES), vol. 1, no. 2, pp. 49-54, Jul. 2012,
  2. S. Kantawong, "Tanasak phanprasit, accident detection system using sensor," Int. J. Inf. Eng., vol. 2, no. 3, pp. 106-115, Sept. 2012,
  3. A. ISuge, H. Takigawa, H. Osuga, H. Soma, and K. Morisaki, "Vehide navigation & information systems," in Proc. IEEE Conf., pp. 45-55, Nagoya, Japan, Jul. 1994.
  4. S. Sadeky, A. Al-Hamadiy, B. Michaelisy, and U. Sayed, "Real-time automatic traffic accident recognition using HFG," 2010 Int. Conf. Pattern Recognition, pp. 3348-3352, Madrid, Spain, Jun. 2010.
  5. J.-W. Hwang, Y.-S. Lee, and S.-B. Cho, "Hierarchical probabilistic network-based system for traffic accident detection at intersections," 2010 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp. 211-217, Jeju, Korea, 2010.
  6. A. Yoneyama, C.-H. Yeh, and C.-C J. Kuo, "Robust vehicle and traffic information extraction for highway surveillance," EURASIP J. Applied Signal Processing, vol. 1, no. 11, pp. 2305-2321, 2005.
  7. B. Coifman, D. Beymer, P. McLauchlan, and J. Malik, "A real-time computer vision system for vehicle tracking and traffic surveillance," Transportation Research, Part C 6, pp. 271-288, 1998. https://doi.org/10.1016/S0968-090X(98)00019-9
  8. J. Oh, J. Min, "Development of a real time video image processing system for vehicle tracking," J. Korean Soc. Road Eng., vol. 10, no. 3, pp. 19-31. Sept. 2008.
  9. H. Liu, J. Li, Q. Liu, and Y. Qian, "Shadow elimination in traffic video segmentation," MVA 2007 IAPR Conf. Machine Vision Appl., pp. 508-514, Tokyo, Japan, May 2007.
  10. S.-C. Chen, M.-L. Shyu, S. Peeta, and C. Zhang, "Learning-based spatio-temporal vehicle tracking and indexing for a transportation multimedia database System," IEEE Trans. Intelligent Transportation Syst., vol. 4, no. 3, pp. 154-167, Sept. 2003. https://doi.org/10.1109/TITS.2003.821290
  11. J. Oh, J. Min, M. Kim, H. Cho, "Development of an automatic traffic conflict detection system based on image tracking technology," TRB, vol. 4, no. 5, pp. 34-42, 2008.
  12. D. Koller, K. Daniilidis, and H. Nagel, "Model-based object tracking in monocular image sequences of road traffic scenes," Int. J. Comput. Vision, vol. 10, pp. 257-281, 1993. https://doi.org/10.1007/BF01539538
  13. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, "Towards robust automatic traffic scene analysis in real time," ICPR, vol. 1, pp. 126-131, 1994.
  14. A. Senior, A. Hampapur, Y.-L. Tian, L. Brown, S. Pankanti, and R. Bolle, "Appearance models for occlusion handling," J. Image and Vision Computing, vol. 24, Issue 11, pp. 1233-1243, Nov. 2006. https://doi.org/10.1016/j.imavis.2005.06.007
  15. R. Cucchiara, C. Grana, G. Tardini, and R. Vezzani, "Probabilistic people tracking for occlusion handling," in Proc. 17th Int. Conf. ICPR 2004, Vol. 1, pp. 132-135, Hongkong, China, Aug. 2004.
  16. I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: real-time surveillance of people and their activities," IEEE Trans. Pattern Analysis and Machine Intelligence(PAMI), vol. 22, no. 8, pp. 301-310. Aug. 2000.
  17. S. L. Dockstader and A. M. Tekalp, "Multiple camera fusion for multi-object tracking," in Proc. IEEE Workshop on Multi-Object Tracking, pp. 95-102, 2001.
  18. J. Melo, A. Naftel, A. Bernardino, and J. Santos-Victor, "Viewpoint independent detection of vehicle trajectories and lane geometry from uncalibrated traffic surveillance cameras," Int. Conf. Image Anal. and Recognition, pp. 1204-1212, Porto, Portugal, Sept. 29-Oct. 1, 2004.
  19. M. Xiao, C.-Z. Han, and L. Zhang, "Moving shadow detection and removal for traffic sequences," Int. J. Automation and Computing, vol. 23. no. 4 pp. 38-46, Jan. 2007.
  20. T. Horprasert, D. Harwood, and L.S. Davis, "A statistical approach for real-time robust background subtraction and shadow detection," in Proc. IEEE ICCV, pp. 1-19, Lasvegas, USA, 1999.
  21. R. P. Avery, G. Zhang, Y. Wang, and N. L. Nihan, "An investigation into shadow removal from traffic images," TRB, vol. 3, no. 2, pp. 987-998, 2007.
  22. S. Kim, S. Oh, K. Kim, S. Park, and K. Park, "Front and rear vehicle detection and tracking in the day and night time using vision and sonar sensors," in Proc. ITS World Congress, pp. 6-10, Amsterdam, Nov. 2005.
  23. Z. Kim, "Real time object tracking based on dynamic feature grouping with background subtraction," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
  24. A. Ukil and R. Zivanovic, "Abrupt Change Detection in Power System Fault Analysis using Wavelet Transform," Int. Conf. Power Systems Transients (IPST'05), pp. 19-23, Montreal, Canada, Jun. 2005