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CCTV-Aided Accident Detection System on Four Lane Highway with Calogero-Moser System
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 Title & Authors
CCTV-Aided Accident Detection System on Four Lane Highway with Calogero-Moser System
Lee, In Jeong;
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 Abstract
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.
 Keywords
Wigner distribution;detection abrupt signal;Calogero-Moser System;
 Language
English
 Cited by
 References
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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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