为了对交叉口车辆的位置进行准确定位, 提出了一种分布式视频网络架构下车辆精确定位方法。在分布式视频网络中每处摄像机架设位置均设有2类摄像机: 近景摄像机和远景摄像机。首先在近景摄像机拍摄范围内, 对感兴趣区域内车辆进行身份识别, 根据车牌照平面与道路平面垂直的约束条件, 建立车牌照模型来对车辆精确定位; 接着在远景摄像机拍摄范围内, 采用融合局部二值模式(LBP)纹理特征的金字塔稀疏光流法实时跟踪车辆上局部特征点, 根据特征点运动趋势相似性获得稳态特征点, 来对车辆位置估计; 最后根据不同摄像机检测结果, 采用加权一致性信息融合算法来提高车辆定位精度。实验结果表明: 该方法能对交叉口车辆位置进行精确定位。
Abstract
A robust framework is given for precise vehicle localization in intersections using distributed video networks. Each intersection is equipped with short-range and long-range cameras in a distributed video network. If the vehicle is in the shooting range of the short-range camera, within the region of interest for vehicle identification, and the license plate is perpendicular to the road plane, a vehicle license plate model is used to accurately locate the vehicle position. If the vehicle is in the shooting range of the long-range camera, a pyramid sparse optical flow algorithm with LBP texture features is used in real-time to track the local feature points on the vehicle to estimate the vehicle position based on stable feature points obtained from the similar motions. Finally, information is exchanged between the cameras, a weighted consensus information fusion algorithm is used to obtain a globally optimal estimate of the vehicle position. Tests show that this method can accurately locate the vehicle position at intersections.
关键词
车辆精确定位 /
分布式视频网络 /
加权一致性信息融合 /
车牌照模型
Key words
precise vehicle location /
distributed video networks /
weighted consensus information fusion /
vehicle license plate model
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参考文献
[1] Buch N, Velastin S A, Orwell J. A review of computer vision techniques for the analysis of urban traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3):920-939.
[2] Ghosh N, Bhanu B. Incremental unsupervised three-dimensional vehicle model learning from video[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(2):423-440.
[3] Chang W C, Cho C W. Online boosting for vehicle detection[J]. IEEE Transactions on Systems, Man and Cybernetics, Part B:Cybernetics. 2010, 40(3):892-902.
[4] Kanhere N K, Birchfield S T. Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features[J]. IEEE Transactions on Intelligent Transportation Systems. 2008, 9(1):148-160.
[5] Bhanu B, Ravishankar C V, Roy-Chowdhury A K, et al. Distributed Video Sensor Networks[M]. London, UK:Springer London, 2011.
[6] Olfati-Saber R, Fax J A, Murray R M. Consensus and cooperation in networked multi-agent systems[J]. In Proceedings of the IEEE, 2007, 95(1):215-233.
[7] Olfati-Saber R. Kalman-consensus filter:optimality, stability, and performance[C]//In IEEE Conference on Decision and Control. Shanghai, China:IEEE Press, 2009:7036-7042.
[8] Kamal A T, Ding C, Song B, et al. A generalized kalman consensus filter for wide area video networks[C]//In IEEE Conference on Decision and Control. Orlando, FL, USA:IEEE Press, 2011:7863-7869.
[9] Kamal A T, Farrell J A, Roy-Chowdhury A K. Information weighted consensus filters and their application in distributed camera networks[J]. IEEE Transactions on Automatic Control, 2013, 58(12):3112-3125.
[10] Yang D L, Chen Y Z, Xin L, et al. Real-time detecting and tracking of traffic shockwaves based on weighted consensus information fusion in distributed video network[J]. IET Intelligent Transport Systems. 2014, 8(4):377-387.
[11] Hofleitner A, Herring R, Bayen A. Arterial travel time forecast with streaming data:a hybrid approach of flow modeling and machine learning[J]. Transportation Research Part B, 2012, 46(9):1097-1122.
[12] Du S, Ibrahim M, Shehata M, et al. Automatic license plate recognition (ALPR):a state-of-the-art review[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(2):311-325.
[13] Mithun N C, Rashid N U, Rahman S M M. Detection and classification of vehicles from video using multiple time-spatial images[J]. IEEE Transactions on Intelligent Transportation Systems. 2012,13(3):1215-1225.
[14] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002,24(7):971-987.
[15] Shi J, Tomasi C. Good features to track[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Seattle:IEEE Press, 1994:593-600.
[16] GA36-2007. 中华人民共和国机动车号牌[S]. 北京:中华人民共和国公安部, 2007. GA36-2007. License plate of motor vehicle of the People's Republic of China[S].Beijing:the ministry of public security of the People's Republic of China, 2007. (in Chinese)
[17] 杨德亮, 谢旭东, 李春文, 等. 基于车牌照模型的大地坐标系下车辆精确定位[J]. 清华大学学报(自然科学版), 2014,54(12):1566-1572. YANG Deliang, XIE Xudong, LI Chunwen, et al. Precise vehicle location under geodetic coordinate based on vehicle license plate model[J]. Journal of Tsinghua University, 2014,54(12):1566-1572. (in Chinese)
[18] Kay S M. Fundamentals of Statistical Signal Processing, Volume I:Estimation Theory[M]. Upper Saddle River, NJ, USA:Prentice Hall, 1993.
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