|Table of Contents|

Detecting and tracking method of moving vehicle(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年04期
Page:
107-113
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Detecting and tracking method of moving vehicle
Author(s):
LOU Lu1;  ZHAO Ling1;  GENG Tao2
1. School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK
Keywords:
intelligent transportation system;  traffic flow;  detecting method;  adaptive background;  vehicle tracking;  Bayesian algorithm;  Kalman filter
PACS:
U491.116
DOI:
-
Abstract:
In order to improve the comprehensive management ability of intelligent transportation systems in cities, a detecting and tracking method of moving vehicle was presented by using video analysis. Considering the pavement environment of urban transport artery and the difference between moving object and the statistical characteristics for road background, an adaptive background updating algorithm was realized based on Bayesian probability criterion, from which foreground image was extracted. Motion detection and real-time tracking were realized for target vehicle in video sequence based on Kalman filter. The traffic flow video collected from a certain urban transport artery of Chongqing was detected by using the proposed method. Experimental result indicates that the video with normal resolution can be processed in time by using the method, and the average detecting accuracy is 94%, so the proposed method has good real-time performance and robustness, and meets the requirement of real-time detecting and tracking vehicles in urban traffic arteries. 2 tabs, 5 figs, 15 refs.

References:

[1] 杨国亮,王志良,牟世堂,等.一种改进的光流算法[J].计算机工程,2006,32(15):187-188,226. YANG Guo-liang, WANG Zhi-liang, MU Shi-tang, et al. An improved optical flow algorithm[J]. Computer Engineering, 2006, 32(15): 187-188, 226.(in Chinese)
[2] 郑 锦,李 波.视频序列中运动对象检测技术的研究现状与展望[J].计算机应用研究,2008,25(12):3534-3540. ZHENG Jin, LI Bo. Prospects and current studies on motion object detection in video sequences[J]. Application Research of Computers, 2008, 25(12): 3534-3540.(in Chinese)
[3] 查成东,王长松,崔 巍.背景差方法在复杂场景条件下的应用[J].计算机工程与设计,2008,29(4):894-895. ZHA Cheng-dong, WANG Chang-song, CUI Wei. Application of background subtraction under complex scene[J]. Computer Engineering and Design, 2008, 29(4): 894-895.(in Chinese)
[4] 田 军,魏振华,武思远.能量法的自适应背景更新算法[J].计算机科学与探索,2009,3(2):218-224. TIAN Jun, WEI Zhen-hua, WU Si-yuan. A self-adaptive background updating algorithm of energy method[J]. Journal of Frontiers of Computer Science and Technology, 2009, 3(2): 218-224.(in Chinese)
[5] SONG K T, TAI J C. Real-time background estimation of traffic imagery using group-based histogram[J]. Journal of Information Science and Engineering, 2008(24): 411-423.
[6] TANIGUCHI H, NAKAMURA T, FURUSAWA H. Methods of traffic flow measurement using spatio-temporal image[C]∥IEEE. Proceedings of 1999 International Conference on Image processing. Kobe: IEEE, 1999: 16-20.
[7] WAKABAYASHI Y, AOKI M. Traffic flow measurement using stereo slit camera[C]∥IEEE. Proceedings of the 7th international Conference on Intelligent Transportation Systems. Washington DC: IEEE, 2004: 7-12.
[8] KOLLER D, WEBER J, HUANG T, et al. Towards robust automatic traffic scene analysis in real-time [C]∥IEEE. Proceeding of the 33rd of IEEE Conference on Pattern Recognition. Jerusalem: IEEE, 1994: 126-131.
[9] JUN G, AGGARWAL J K, GOKMEN M. Tracking and segmentation of highway vehicles in cluttered and crowded scenes[C]∥IEEE. Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision. Copper Mountain: IEEE, 2008: 1-6.
[10] 赵 宇.视频处理中的目标分割与跟踪的研究[D].北京:中国科学院,2004. ZHAO Yu. The research of object segmentation and tracking in video processing[D]. Beijing: Chinese Academy of Sciences, 2004.(in Chinese)
[11] TAMERSOY B, AGGARWAL J K. Robust vehicle detection for tracking in highway surveillance videos using unsupervised learning[C]∥IEEE. Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. Genova: IEEE, 2009: 529-534.
[12] LI Li-yuan, HUANG Wei-min, GUI Y, et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Transactions on Image Processing, 2004, 13(11): 1459-1472.
[13] 袁基炜,史忠科.一种基于灰色预测模型GM(1,1)的运动车辆跟踪方法[J].控制与决策,2006,21(3):300-304. YUAN Ji-wei, SHI Zhong-ke. A method of vehicle tracking based on GM(1,1)[J]. Control and Decision, 2006, 21(3): 300-304.(in Chinese)
[14] KALMAN R E. A new approach to linear filtering and prediction problems[J]. Transactions of the ASME—Journal of Basic Engineering, 1960(82): 35-45.
[15] STORVIK G. Particle filters for state space models with the presence of unknown static parameters[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 281-289.

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Last Update: 2012-08-30