基于红外与可见光的多源传感器协同检测与跟踪技术研究
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摘要
运动目标检测与跟踪技术是计算机视觉领域重要的研究方向之一,在现代工业、农业、国防以及空间技术等领域都有着广泛的应用。然而,在实际应用中,由于单一传感器往往不能提供足够的场景信息,所以,基于多源传感器的协同检测与跟踪技术已经成为当前的研究热点。
本文基于红外与可见光模式,研究了多源传感器协同检测与跟踪技术,主要工作有:
(1)研究了运动目标检测与提取技术。提出了一种基于自适应背景模型的运动目标检测法,并在此基础上,实现了目标提取。在多种复杂的自然环境下的实验表明,该方法能够较好地完成运动目标的检测和提取。
(2)研究了运动目标跟踪技术。详细介绍了运动预测技术,并在此基础上,提出了一种基于扩展卡尔曼滤波器的运动目标跟踪方法,实验证明该方法具有一定的实际应用价值。
(3)研究了单源跟踪结果评价因子和多源跟踪结果综合评价技术。首先研究分析了单源跟踪结果,确定了四个评价因子:目标面积惯性、目标宽长比惯性、目标位置惯性和目标速度惯性,并对各种评价因子建立了具体的数学模型。其次在单源跟踪结果评价的基础上,研究了多源跟踪结果综合评价技术,提出了基于灰色关联分析理论的综合评价技术。最后进行了仿真验证,结果证明了所建数学模型的正确性以及所提出方法的有效性。
(4)研究了图像配准技术。针对红外与可见光图像,提出了两种图像配准方法,一个是基于子图像边缘相关性的红外与可见光图像配准方法,另一个是基于角点的图像配准方法,实验验证了这两种方法的有效性。
Moving target detection and tracking technology is one of the most important research direction in the field of computer vision, and widely used in modern industry, agriculture, national defense and space technology. However, a single sensor cannot provide sufficient information of the scene in practical applications, so multi-sensor collaborative detection and tracking technology has become hot spots now.
This paper focus on the multi-sensor collaborative detection and tracking technology based on infrared and visual images. The main work is as follows.
(1) Moving target detection and extraction technologies are studied. A moving target detection method based on adaptive background model is adopted to achieve target detection, and on this basis, target extraction is accomplished. Experiments in complex natural environment show that the method can complete the moving target detection and extraction better.
(2) Moving target tracking technology is researched. Movement prediction technology is introduced in detail, and on this basis, a moving target tracking method based on extended Kalman filter is brought forward. Experiments show that the method has some practical value.
(3) The evaluation factors of single-sensor tracking results and synthesized evaluation technology of multi-sensor tracking results are studied. First, single-sensor tracking results are analysed. Four evaluation factors are chosen: target area inertia, target width length ratio inertia, target displacement inertia and target speed inertia. Specific mathematical models are established for all evaluation factors. Second, synthesized evaluation technology of multi-sensor tracking results is studied based on the single-sensor evaluation. A multi-sensor evaluation method based on the theory of gray relational analysis is proposed. Finally, the simulation is carried out, and the results prove the correctness of the mathematical model and the effectiveness of the method.
(4) The image registration technology is studied. Two image registration methods are put forward for infrared and visual images. One is based on the edge relevance of the sub-image, and the other is based on the corner of the image. Experients show that the algorithms are effective.
本文基于红外与可见光模式,研究了多源传感器协同检测与跟踪技术,主要工作有:
(1)研究了运动目标检测与提取技术。提出了一种基于自适应背景模型的运动目标检测法,并在此基础上,实现了目标提取。在多种复杂的自然环境下的实验表明,该方法能够较好地完成运动目标的检测和提取。
(2)研究了运动目标跟踪技术。详细介绍了运动预测技术,并在此基础上,提出了一种基于扩展卡尔曼滤波器的运动目标跟踪方法,实验证明该方法具有一定的实际应用价值。
(3)研究了单源跟踪结果评价因子和多源跟踪结果综合评价技术。首先研究分析了单源跟踪结果,确定了四个评价因子:目标面积惯性、目标宽长比惯性、目标位置惯性和目标速度惯性,并对各种评价因子建立了具体的数学模型。其次在单源跟踪结果评价的基础上,研究了多源跟踪结果综合评价技术,提出了基于灰色关联分析理论的综合评价技术。最后进行了仿真验证,结果证明了所建数学模型的正确性以及所提出方法的有效性。
(4)研究了图像配准技术。针对红外与可见光图像,提出了两种图像配准方法,一个是基于子图像边缘相关性的红外与可见光图像配准方法,另一个是基于角点的图像配准方法,实验验证了这两种方法的有效性。
Moving target detection and tracking technology is one of the most important research direction in the field of computer vision, and widely used in modern industry, agriculture, national defense and space technology. However, a single sensor cannot provide sufficient information of the scene in practical applications, so multi-sensor collaborative detection and tracking technology has become hot spots now.
This paper focus on the multi-sensor collaborative detection and tracking technology based on infrared and visual images. The main work is as follows.
(1) Moving target detection and extraction technologies are studied. A moving target detection method based on adaptive background model is adopted to achieve target detection, and on this basis, target extraction is accomplished. Experiments in complex natural environment show that the method can complete the moving target detection and extraction better.
(2) Moving target tracking technology is researched. Movement prediction technology is introduced in detail, and on this basis, a moving target tracking method based on extended Kalman filter is brought forward. Experiments show that the method has some practical value.
(3) The evaluation factors of single-sensor tracking results and synthesized evaluation technology of multi-sensor tracking results are studied. First, single-sensor tracking results are analysed. Four evaluation factors are chosen: target area inertia, target width length ratio inertia, target displacement inertia and target speed inertia. Specific mathematical models are established for all evaluation factors. Second, synthesized evaluation technology of multi-sensor tracking results is studied based on the single-sensor evaluation. A multi-sensor evaluation method based on the theory of gray relational analysis is proposed. Finally, the simulation is carried out, and the results prove the correctness of the mathematical model and the effectiveness of the method.
(4) The image registration technology is studied. Two image registration methods are put forward for infrared and visual images. One is based on the edge relevance of the sub-image, and the other is based on the corner of the image. Experients show that the algorithms are effective.
引文
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[4]熊大容.杨烜.基于多传感器信息融合的远距离目标检测[J].红外技术.2006,28(12):695-698.
[5]Heesung Kwon,Sandor Z.Der,Nasser M.Nasrabadi.Adaptive multisensor target detection using feature-based fusion[J].Optical Engineering,2002,41(1):69-80.
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[7]朱毅,张朝亮.周君.视频监控系统中的图像预处理技术[J].计算机与数字工程,2007,35(11):102-104.
[8]张小虎.靶场图像运动目标检测与跟踪技术研究[D].长沙:国防科学技术大学,2006.
[9]Haritaoglu I.,Harwood D.,Davis L.S..W~4:Real-time surveillance of people and their activities[J].IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(8):809-830.
[10]C.Anderson,P.Bert,G.Van der Wal.Change detection and tracking using pyramids transformation techniques[J].In Proc of SPIE Intelligent Robics and Computer Vision.1985,579:72-78.
[11]郑江滨.视频监视方法研究[D].西安:西北工业大学,2002.
[12]王栓,艾海舟.何克忠.基于差分图象的多运动目标的检测与跟踪[J].中国图象图形学报.1999.4(6):1057-1012.
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[16]G.P.Stein.Tracking from multiple view Points:Self-calibration of space and time[J].Computer Vision and Pattern Recognition,1999,1:521-527.
[17]杨根.复杂背景下运动目标的协同检测技术研究[D].西安:西北工业大学,2007.
[18]Nagel H..Displacement Vectors Derived From Second-Order Intensity Variations in Image Sequences[J].Computer Graphics Image Processing,1983,10(9):85-117.
[19]Horn B.,Schunck.Determing Optical Flow[J].Artificial Intelligence,1981,17(8):185-203.
[20]刘贵喜,邵明礼,刘先红,等.真实场景下视频运动目标自动提取方法[J].光学技术,2006,26(8):1150-1155.
[21]Li Gang,Zeng Rui-li,Lin Ling.Moving Target Detection in Video Monitoring System[J].The IEEE Sixth World Congress on Intelligent Control and Automation,2006,(2):9778-9781.
[22]M.Kim,J.G..Choi,D.Kim,et al.A VOP Generation Tool:Automatic Segmentation of Moving Objects in Image Sequences Based on Spatio-Temporal Information[J].IEEE Transactions on Circuits and Systems for Video Technology,1999,9(8):1216-1226.
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[24]娄越,相里斌,刘波.基于背景粗糙度估计的红外目标检测算法[J].光子学报, 2007.36(9):1759-1763.
[25]刘文.刘朝晖.熊仁生.一种利用窗口结构提取红外弱小目标的方法[J].光子学报.2008.37(3):618-620.
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