JCC  Vol.3 No.11 , November 2015
Analysis of Abnormal Vehicle Behavior Based on Trajectory Fitting
Abstract

In order to analysis the abnormal vehicle behavior by trajectory fitting effectively, the whole process is divided into three steps: target detection and tracking, vehicle trajectory analysis, vehicle behavior detection. Firstly, a three-frame-differencing method is used to achieve initial target location and an improved tracking algorithm based on Kalman predictor is proposed; then, an adaptive segmented linear fitting algorithm is proposed to achieve vehicle trajectory fitting; finally, two parameters including the rate of velocity variation and the rate of direction variation are used to establish vehicle abnormal behavior detection model. Experiment result shows that the three high dangerous vehicle behaviors in road surveillance videos can be detected effectively: sharp brake, sharp turn, and sharp turn brake.


Cite this paper
Jiang, E. and Wang, X. (2015) Analysis of Abnormal Vehicle Behavior Based on Trajectory Fitting. Journal of Computer and Communications, 3, 13-18. doi: 10.4236/jcc.2015.311003.
References

[1]   Versavel, J. (1999) Road Safety through Video Detection. 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, 753-757. http://dx.doi.org/10.1109/itsc.1999.821155

[2]   Qimei, L.B.C. (2006) Vehicle Activity Analysis from Freeway Traffic Video. Chinese Journal of Scientific Instrument, S3. (In Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-YQXB2006S3139.htm

[3]   Zhao, X.M., Hui, F., Shi, X., Ma, J.Y. and Yang, L. (2014) Concept, Architecture and Challenging Technologies of Ubiquitous Traffic Information Service System. Journal of Traffic and Transportation Engineering, 105-115. (In Chinese) http://www.cnki.com.cn/Article/CJFDTotal-JYGC201404015.htm

[4]   Zhao, Y.T., Li, X.Y. and Luo, D.H. (2011) Study on the Methods of Automatic Incident Detection Based on the Video Vehicle Trajectory Model. Acta Scientiarum Naturalium Universitatis Sunyatseni, 56-60. (In Chinese) http://www.cnki.com.cn/Article/CJFDTotal-ZSDZ201104012.htm

[5]   Wang, W.G. and Ma, R.G. (2013) Weighed Support Vector Machine for Traffic Incident Detection. Journal of Chang’an University (Natural Science Edition), 6, 014. (In Chinese) http://www.cnki.com.cn/Article/CJFDTotal-XAGL201306014.htm

[6]   Liu, Q.C., Lu, J. and Chen, S.Y. (2014) Design and Analysis of Traffic Incident Detection Based on Random Forest. Journal of Southeast University (English Edition), 1, 88-95. http://en.cnki.com.cn/Article_en/CJFDTotal-DNDY201401017.htm

[7]   Amin, M.S., Reaz, M.B.I. and Nasir, S.S. (2014) Integrated Vehicle Accident Detection and Location System. TELKOMNIKA (Telecommunication Computing Electronics and Control), 12, 73-78. http://jogjapress.com/index.php/TELKOMNIKA/article/view/1787 http://dx.doi.org/10.12928/telkomnika.v12i1.13

[8]   Yan, J.H., Chen, S.H. and Ai, S.F. (2014) Target Tracking with Improved CAMShift Based on Kalman Predictor. Journal of Chinese Inertial Technology, 4, 021. (In Chinese) http://www.cnki.com.cn/Article/CJFDTotal-ZGXJ201404021.htm

[9]   Bradski, G.R. (1998) Computer Vision Face Tracking for Use in a Perceptual User Interface. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.7673

[10]   Liu, R.M., Li, X.L., Han, L. and Meng, J. (2013) Track Infrared Point Targets Based on Projection Coefficient Templates and Non-Linear Correlation Combined with Kalman Prediction. Infrared Physics & Technology, 57, 68-75. http://www.sciencedirect.com/science/article/pii/S1350449512001193 http://dx.doi.org/10.1016/j.infrared.2012.12.011

[11]   Zhang, X.Y. and Da, Q.D. (2001) Technology of Highway Automatic Incident Detection. Systems Engineering-Theory & Practice, 21, 118-124. (In Chinese) http://www.cnki.com.cn/Article/CJFDTotal-XTLL200106021.htm

 
 
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