A Statistical Framework for Real-Time Traffic Accident Recognition
Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed
DOI: 10.4236/jsip.2010.11008   PDF    HTML     6,603 Downloads   12,366 Views   Citations


Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.

Share and Cite:

S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, "A Statistical Framework for Real-Time Traffic Accident Recognition," Journal of Signal and Information Processing, Vol. 1 No. 1, 2010, pp. 77-81. doi: 10.4236/jsip.2010.11008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. Thompson, J. Whitem, B. Dougherty, A. Albright and D. C. Schmidt, “Using Smartphones and Wireless Mobile Networks to Detect Car Accidents and Provide Situational Awareness to Emergency Responders,” 3rd International ICST Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, Mobilware, Chicago, 30 June-2 July 2010.
[2] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “Real-Time Automatic Traffic Accident Recognition Using HFG,” Proceedings of the 20th International conference on Pattern Recognition (ICPR 10), Istanbul, Turkey, 2010, pp. 3348-3351.
[3] Y.-H. Wen, T.-T. Lee, and H.-J. Cho, “Hybrid Models Toward Traffic Detector Data Treatment and Data Fusion,” Proc. of IEEE Confrence on Networking, Sensing and Control, pp. 525-530, 2005.
[4] C. F. Lai, C. Y. Liu, S.-Y. Chang and Y. M. Huang, “Portable Automatic Conjecturing and Announcing System for Real-Time Accident Detection,” International Journal on Smart Sensing And Intelligent Systems, Vol. 2, No. 2, June 2009.
[5] M. Meler, “Car Color and Logo Recognition,” CSE 190 A Projects in Vision and Learning, University of California 2006.
[6] D. Srinivasan, W. H. Loo and R. L. Cheu, “Traffic Incident Detection Using Particle Swarm Optimization,” Proceedings of the IEEE International Intelligence Symposium, 2003, pp. 144-151.
[7] D. Srinivasan, R. L. Cheu and Y. P. Poh, “Hybrid Fuzzy Logic-Genetic Algorithm Technique for Automated Detection of Traffic Incidents on Freeways,” Proceedings of the IEEE Intelligent Transportation Systems Conference, Oakland, 2002, pp. 352-357.
[8] S. M. Tang, X. Y. Gong and F. Y. Wang, “Traffic Incident Detection Algorithm Based on Non-parameter Regression,” Proceedings of the IEEE 5th Intelligent Trans- portation Systems Conference, Singapore, 2002, pp. 714- 719.
[9] Y. Murai, H. Fujiyoshi and M. Kazui, “Incident Detection based on Dynamic Background Modeling and Statistical Learning using Spatio-temporal Features,” Proceedings of the MVA 2009 IAPR Conference on Machine Vision Applications, Yokohama, Japan, 2009, pp. 156-161.
[10] M. J. Cassidy, S. B. Anani and J. M. Haigwood, “Study of Freeway Traffic near an Off-Ramp”, Transportation Research Part A: Policy and Practice, Vol. 36, 2002, pp. 563-572.
[11] H. Ikeda, T. Matsuo, Y. Kaneko and K. Tsuji, “Abnormal Incident Detection System Employing Image Processing Technology,” Procedings of the IEEE Conference Vehicle Navigation and Information Systems, Tokyo, Japan, 1999, pp. 748-752.
[12] W. Hu, X. Xiao, T. Tan and S. Maybank, “Traffic Accident Prediction Using 3-D Model-Based Vehicle Tracking,” IEEE Transaction on Vehcile Technology, Vol. 53, 2004, pp. 677-693.
[13] M. Kimachi, K. Kanayama and K. Teramoto, “Incident Prediction by Fuzzy Image Sequence Analysis,” Proceedings of the IEEE International Conference Vehicle Navigation and Information Systems (VNIS'94), pp. 51-57, 1994.
[14] D. Zeng, J. Xu and G. Xu, “Data Fusion for Traffic Incident Detection Using D-S Evidence Theory with Probabilistic SVMs,” Journal of Computers, Vol. 3, 2008, pp. 36-43.
[15] B. D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proceedings of Imaging Understanding Workshop, 1981, pp. 121-130.
[16] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, 2005, pp. 886-893.
[17] J. M. Hilbe, “Logistic Regression Models,” Chapman & Hall/CRC Press, London. 2009.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.