基于视觉感知的鱼群目标检测与跟踪技术研究
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摘要
生物式水质监控监测技术是指利用生物个体、种群或群落对环境污染所产生的反应,利用相关生物学方法,运用生物学视角对环境状况进行监测和评价的一种技术,其监测结果直接反映水环境质量状况。利用生物特征的变化来反映水质情况,是实现水质监测的一种有效的手段。因此,如何快速有效地获取相关生物的运动特征,是生物水质监测方法的主要研究内容。
本文以鱼群作为生物式水质监控的指示物体,主要研究了计算机视觉在生物式水质监测系统中应用,其主要目的是获取与生物式水质监测有关的鱼群运动特征,建立一套完整的生物式水质监控体系。研究内容主要包括:鱼群目标的实时检测与跟踪、鱼群监测平台与系统的设计。主要研究工作归纳如下:
1.研究了基于图割的运动目标检测方法
提出了一种新的基于图割的运动目标检测算法,该算法首先运用分水岭方法对视频图像进行预处理,把图像分割成若干区域,在每个区域中分别构建网络图结构,然后构造能量函数,该函数包括:软约束、硬约束和时间约束。最后,分别在每个网络中求解最小化能量函数,找到最小割。
2.研究了基于Delaunay三角网的鱼群目标跟踪算法
提出了Delaunay三角网的群目标跟踪算法。该算法首先在检测后的图像中提取每条鱼的坐标点。然后,利用这些点构建Delaunay三角网,接着计算这些点的群目标力度。最后根据设定的阈值把适合的目标归入到群目标,并计算其群目标中心,达到群目标跟踪的目的。
3.研究了基于视觉感知的鱼群运动监控平台与系统设计
为了营造与真实环境比较接近的实验环境,对现有的实验平台作了适当改进。同时设计了基于视觉感知的鱼群目标跟踪系统体系结构,初步开发了鱼群目标的视频采集模块和跟踪模块。
Biological monitoring techniques for water quality are technology, the basic principle of which is that using the reaction of biological individual, population and group to environmental pollution to expound the pollution state of the environment. The result of monitoring techniques can reflect the change of the water quality of water environment directly and real-time. Using the changes of biodiversity characteristics to monitoring the water quality is an effective way. So, how to get the characteristics of biodiversity behavior is a very important part of the water quality monitoring system
In order to get the information of characteristics of fish group to make a completed water quality monitoring system, this paper used fish group as the indicator organism of biomonitoring for water quality monitoring, and mainly study the outline of computer vision applications in movement behavior monitoring by fish group. The main content of the article includes: real-time detecting fish group, tracking fish group object and design and realization for the monitoring System and platform. The main results of this paper summarized as follows:
First, Study on real-time detection method for the moving objects Based on graph cut. A new moving object detection algorithm based on graph cut. Firstly, a method of watershed transform is adopted to divide the image into parts, and build the net in every part. Then a new energy function was constructed, which contain soft constraints, hard constraints and time constraints. Finally, get the minimum cut by minimizing the energy function by graph cut.
Second, Study on fish group tracing Based on delaunay triangulation. In order to accurately track fish group, a new algorithm is proposed, which is based on the traditional background subtraction algorithm and the Delaunay Triangulation network. At first, the traditional background subtraction algorithm is used to deal with the video image sequence; Then, the coordinates of each fish are evaluated; Third, the constraint Delaunay Triangulation is established and some objects are removed so that fish group can be detected. Experimental results show that this algorithm can track fish groups accurately and provide effective data for monitoring water quality.
Last, study on the system and platform of fish group detecting and tracking based on computer vision. In order to make the experiment environment to similar to the real environment, the existed experiment platform has been appropriately modified. The architecture of fish group detecting and tracking based on computer vision has been designed and vision processing model and fish group tracking model initially constructed.
本文以鱼群作为生物式水质监控的指示物体,主要研究了计算机视觉在生物式水质监测系统中应用,其主要目的是获取与生物式水质监测有关的鱼群运动特征,建立一套完整的生物式水质监控体系。研究内容主要包括:鱼群目标的实时检测与跟踪、鱼群监测平台与系统的设计。主要研究工作归纳如下:
1.研究了基于图割的运动目标检测方法
提出了一种新的基于图割的运动目标检测算法,该算法首先运用分水岭方法对视频图像进行预处理,把图像分割成若干区域,在每个区域中分别构建网络图结构,然后构造能量函数,该函数包括:软约束、硬约束和时间约束。最后,分别在每个网络中求解最小化能量函数,找到最小割。
2.研究了基于Delaunay三角网的鱼群目标跟踪算法
提出了Delaunay三角网的群目标跟踪算法。该算法首先在检测后的图像中提取每条鱼的坐标点。然后,利用这些点构建Delaunay三角网,接着计算这些点的群目标力度。最后根据设定的阈值把适合的目标归入到群目标,并计算其群目标中心,达到群目标跟踪的目的。
3.研究了基于视觉感知的鱼群运动监控平台与系统设计
为了营造与真实环境比较接近的实验环境,对现有的实验平台作了适当改进。同时设计了基于视觉感知的鱼群目标跟踪系统体系结构,初步开发了鱼群目标的视频采集模块和跟踪模块。
Biological monitoring techniques for water quality are technology, the basic principle of which is that using the reaction of biological individual, population and group to environmental pollution to expound the pollution state of the environment. The result of monitoring techniques can reflect the change of the water quality of water environment directly and real-time. Using the changes of biodiversity characteristics to monitoring the water quality is an effective way. So, how to get the characteristics of biodiversity behavior is a very important part of the water quality monitoring system
In order to get the information of characteristics of fish group to make a completed water quality monitoring system, this paper used fish group as the indicator organism of biomonitoring for water quality monitoring, and mainly study the outline of computer vision applications in movement behavior monitoring by fish group. The main content of the article includes: real-time detecting fish group, tracking fish group object and design and realization for the monitoring System and platform. The main results of this paper summarized as follows:
First, Study on real-time detection method for the moving objects Based on graph cut. A new moving object detection algorithm based on graph cut. Firstly, a method of watershed transform is adopted to divide the image into parts, and build the net in every part. Then a new energy function was constructed, which contain soft constraints, hard constraints and time constraints. Finally, get the minimum cut by minimizing the energy function by graph cut.
Second, Study on fish group tracing Based on delaunay triangulation. In order to accurately track fish group, a new algorithm is proposed, which is based on the traditional background subtraction algorithm and the Delaunay Triangulation network. At first, the traditional background subtraction algorithm is used to deal with the video image sequence; Then, the coordinates of each fish are evaluated; Third, the constraint Delaunay Triangulation is established and some objects are removed so that fish group can be detected. Experimental results show that this algorithm can track fish groups accurately and provide effective data for monitoring water quality.
Last, study on the system and platform of fish group detecting and tracking based on computer vision. In order to make the experiment environment to similar to the real environment, the existed experiment platform has been appropriately modified. The architecture of fish group detecting and tracking based on computer vision has been designed and vision processing model and fish group tracking model initially constructed.
引文
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[3]谢建春.水体污染与水生动物[J].生物学通报, 2001, 36(6): 10-11.
[4]马文漪,杨柳燕.环境微生物工程[M].南京:南京大学出版社, 1998.
[5]王海洲,刘文华,侯福林.在线生物监测技术及其应用研究[J].生物学通报, 2007, 42(1): 15-16.
[6]凯思斯著,曹凤中译.水污染的生物监测[M].北京:中国环境科学出版社, 1989.
[7]北京正兴联合电机有限公司.生物传感器[N].中国水利, 2007, 22: 72:72.
[8]李志良.鱼类行为学在水质在线监测与预警中的应用研究[D].山东师范大学, 2008, 04.
[9]横田陆郎,王黎,唐川钟,阿丽娜,李波,腾晓鹏.毒性物质的快速生物监测方法与应用[J].沈阳化工大学学报. 2010, 24(3): 283-288.
[10] Morgan W. S. G. Fish locomotor behaviour paaems as a monitoring tool[J]. Wat. Pollut Control, 1979, 51: 580-589.
[11]房英春,刘广纯,田春,何小惠,宋钢.浅析河流水体污染的生物监测及指示生物[J].水土保持研究, 2005, 12(2): 151-153.
[12] Manju G., Asha C., Malhotra B. Application of conducting polymers to biosensors[J]. Biosensors & Bioelectronics, 2002, 17: 345-359.
[13]许木启,曹宏,王玉龙.原生动物群落多样性变化与汉沽稳定塘水质净化效能相互关系的研究[J].生态学报, 2002, 20(2): 283-287.
[14]郑怀礼,龚迎昆.用于环境监测的生物传感技术[J].光谱学与光谱分析, 2003, 23(2): 411-414.
[15] Kumaran R., Bengt D. Principles and applications of thermal biosensors[J]. Biosensors & Bioelectronics, 2001, 16: 417-423.
[16]张志杰,张维平.环境污染生物监测与评价[M].第一版.北京:中国环境科学出版社,1991.
[17]高继军,张力平,马梅.应用淡水发光菌研究二元重金属混合物的联合毒性[J].上海环境科学, 2003, 22(11):772-775.
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