混合动态纹理与李群论相结合的交通异常事件监测技术
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摘要
交通流视频背景较为复杂。当前交通异常事件监测技术无法有效抑制复杂背景下的噪声干扰,导致监测精度低下。为此,提出一种新的混合动态纹理与李群论相结合的交通异常事件监测技术,通过Gabor滤波器对交通视频图像进行滤波处理;采集图像纹理信息,通过时空方向能量对交通异常事件图像动态纹理特征进行提取。依据李群空间中仿射群组不受外界干扰能够保持形状的特性,把交通异常事件状态变量映射至李群空间进行处理。介绍了李群和李代数之间的空间映射关系,给出交通异常事件状态方程和相似匹配度量准则。利用跟踪图像的加权均值对跟踪模板进行更新,实现交通异常事件的监测。实验结果表明,所提技术监测时效和精度均较高。
The background of traffic flow video is complex,and the current traffic anomaly monitoring technology can not effectively suppress the noise interference in the complex background,resulting in low monitoring accuracy. To this end,the traffic incident monitoring technology of a new hybrid dynamic texture and Lie group theory combined,through the filtering of the traffic video image Gabor filter,image texture information,based on the traffic incident dynamic image texture feature of temporal direction of energy extraction. On the basis of the affine Li Qun space group without outside interference characteristics to maintain the shape of the traffic incident state variables mapped to the Li Qun space,the space mapping relationship between Li Qun and the lie algebra,given the traffic incident state equation and similarity matching measurement criteria,the tracking template is updated using the weighted average image tracking the implementation of monitoring of traffic incident. The experimental results show that the proposed technique has high accuracy and efficiency.
The background of traffic flow video is complex,and the current traffic anomaly monitoring technology can not effectively suppress the noise interference in the complex background,resulting in low monitoring accuracy. To this end,the traffic incident monitoring technology of a new hybrid dynamic texture and Lie group theory combined,through the filtering of the traffic video image Gabor filter,image texture information,based on the traffic incident dynamic image texture feature of temporal direction of energy extraction. On the basis of the affine Li Qun space group without outside interference characteristics to maintain the shape of the traffic incident state variables mapped to the Li Qun space,the space mapping relationship between Li Qun and the lie algebra,given the traffic incident state equation and similarity matching measurement criteria,the tracking template is updated using the weighted average image tracking the implementation of monitoring of traffic incident. The experimental results show that the proposed technique has high accuracy and efficiency.
引文
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16吴士力.基于李群理论的汽油发动机尾气分析模型匹配算法研究.交通节能与环保,2015;11(6):37-42Wu Shili.The research for gasoline engine exhaust model based on Lie Group theory.Energy Conservation&Environmental Protection in Transportation,2015;11(6):37-42
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18刘海涛.恒虚警下异常网络流量序列监测技术仿真分析.计算机仿真,2016;33(8):244-247Liu Haitao.Under the constant false alarm monitoring technology simulation analysis abnormal network flow sequence.Computer simulation,2016;33(8):244-247
19蔡劲.大型远程行人晕厥无线监测系统关键算法设计.科技通报,2015;31(10):43-45Cai Jin.Key algorithm design of large remote wireless monitoring system for pedestrian syncope.Bulletin of Science and Technology,2015;31(1):43-45
20 Spratt S E,Batch B C,Davis L P,et al.Methods and initial findings from the Durham Diabetes Coalition:integrating geospatial health technology and community interventions to reduce death and disability.Journal of Clinical&Translational Endocrinology,2015;2(1):26-36
2万福才,尹承祥,韩晓微,等.城市道路交通异常事件自动检测方法.沈阳大学学报(自然科学版),2015;27(1):30-33Wan Fucai,Yin Chengxiang,Han Xiaowei,et al.Automatic detection method of abnormal events in city road traffic.Journal of Shenyang University(Natural Science),2015;27(1):30-33
3 Han M S.Performance evaluation of dynamic bandwidth allocation algorithm for XG-PON using traffic monitoring and intra-scheduling.International Journal of Software Engineering&Its Applications,2015;9(8):207-216
4 Karim A,Salleh R B,Shiraz M,et al.Botnet detection techniques:review,future trends,and issues.Frontiers of Information Technology&Electronic Engineering,2014;15(11):943-983
5向怀坤,李伟龙,谢秉磊.粒子群优化神经网络的交通事件检测算法研究.计算机测量与控制,2016;24(2):171-174Xiang Huaikun,Li Weilong,Xie Binglei.Research on traffic inciden detection algorithm based on particle swarm optimizer neural network.Computer Measurement and Control,2016;24(2):171-174
6李倩丽,宋焕生.基于视频车辆运动轨迹场的交通事件检测方法.电视技术,2015;39(13):50-52Li Qianli,Song Huansheng.Method of detecting traffic incident based on vehicle trajectory field.Video Engineering,2015;39(13):50-52
7 Yu J,Karaman S,Rus D.Persistent monitoring of events with stochastic arrivals at multiple stations.IEEE Transactions on Robotics,2015;31(3):521-535
8陈岳明,杨帆.异常突发事件下交通流特征分析与预测.科学技术与工程,2016;16(23):75-78Chen Yueming,Yang Fan.Research on urban traffic flow characteristic and prediction under abnormal states.Science Technology and Engineering,2016;16(23):75-78
9李鑫,陈艳艳,陈宁,等.通道行人集聚型异常事件自动识别算法设计.公路交通科技,2016;33(5):121-127Li Xin,Chen Yanyan,Chen Ning.Design of automatic identification algorithm for pedestrian clustering in channel.Journal of Highway and Transportation Research and Development,2016;33(5):121-127
10 Chen B H,Huang S C.An advanced moving object detection algorithm for automatic traffic monitoring in real-world limited bandwidth networks.IEEE Transactions on Multimedia,2014;16(3):837-847
11李林,刘建华.基于NFC的高速公路异常事件预警系统设计.计算机与数字工程,2016;44(8):1498-1503Li Lin,Lin Jianhua.Design of the NFC based expressway traffic incidents early warning system.Computer and Digital Engineering,2016;44(8):1498-1503
12浦倩云,吴锡生.基于改进蝙蝠算法的无线分簇路由算法.电子设计工程,2016;24(9):126-128Pu Qianyun,Wu Xisheng.Wireless clustering routing algorithm based on improved bat algorithm.Electronic Design Engineering,2016;24(9):126-128
13 Hruska B,Irish L A,Pacella M L,et al.PTSD symptom severity and psychiatric comorbidity in recent motor vehicle accident victims:a latent class analysis.Journal of Anxiety Disorders,2014;28(7):644-649
14陈艳艳,陈宁,王杨,等.一种考虑吸引型异常事件的行人元胞自动机模型.交通运输系统工程与信息,2015;15(3):56-63Chen Yanyan,Chen Ning,Wang Yang.A cellular automata model considering attractive incidents.Journal of Transportation Systems Engineering and Information Technology,2015;15(3):56-63
15刘洋,李一波,姬晓飞,等.基于稀疏编码的动态纹理识别.中国图象图形学报,2014;19(8):1185-1193Liu Yang,Li Yibo,Ji Xiaofei,et al.Dynamic texture recognition based on sparse coding.Journal of Image and Graphics,2014;19(8):1185-1193
16吴士力.基于李群理论的汽油发动机尾气分析模型匹配算法研究.交通节能与环保,2015;11(6):37-42Wu Shili.The research for gasoline engine exhaust model based on Lie Group theory.Energy Conservation&Environmental Protection in Transportation,2015;11(6):37-42
17 Ashok K P M,Vaidehi V.Anomalous event detection in traffic video based on sequential temporal patterns of spatial interval events.KSIITransactions on Internet&Information Systems,2015;9(1):169-189
18刘海涛.恒虚警下异常网络流量序列监测技术仿真分析.计算机仿真,2016;33(8):244-247Liu Haitao.Under the constant false alarm monitoring technology simulation analysis abnormal network flow sequence.Computer simulation,2016;33(8):244-247
19蔡劲.大型远程行人晕厥无线监测系统关键算法设计.科技通报,2015;31(10):43-45Cai Jin.Key algorithm design of large remote wireless monitoring system for pedestrian syncope.Bulletin of Science and Technology,2015;31(1):43-45
20 Spratt S E,Batch B C,Davis L P,et al.Methods and initial findings from the Durham Diabetes Coalition:integrating geospatial health technology and community interventions to reduce death and disability.Journal of Clinical&Translational Endocrinology,2015;2(1):26-36