Moving object detection of Ochotona curzoniae based on spatio-temporal imformation

doi:10.11975/j.issn.1002-6819.2018.09.024

	Moving object detection of Ochotona curzoniae based on spatio-temporal imformation
	Zhang, Aihua1,3,4 ; Wang, Fan 2; Chen, Haiyan2
	2018-05-01
发表期刊	Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
ISSN	10026819
卷号	34 期号:9 页码:197-203
摘要	Ochotona curzoniae is an endemic species and key species in the alpine meadow of the Tibetan Plateau and also it is a main kind of organism that destroys the grassland ecology of the plateau. In order to prevent the dangers of the ochotona curzoniae, we need to study the living habits of ochotona curzoniae and investigate the degree of harm of ochotona curzoniae, and then we can control the number of ochotona curzoniae through the effective preventive measures. With the development of sensing technology and image processing, we can provide an objective basis through intelligent monitoring system to control the damage of ochotona curzoniae. The object detection of ochotona curzoniae is a key technology in the intelligent monitoring equipment because it can provide the object contour feature for behavior analysis of ochotona curzoniae. The object detection of ochotona curzoniae is very difficult, because the ochotona curzoniae video possesses the characteristics of complex background, low contrast, the object color with intensity inhomogeneity, diversity and mutability. The traditional object detection method cannot extract the object contours accurately. This paper presents a fast object detection method based on space-time domain. Firstly, the centroid position of the object in the current frame image is determined by the background subtraction, and then the rough segmented image and the initial contour are obtained based on the centroid position. The rough segmented image is segmented by the improved Chan-Vese model, and then we can obtain the object contours. In view of the fact that the level set function needs to be initialized in the process of improved Chan-Vese model, and the initialized computation is enhanced with the increase of the image scale, the centroid of the object is taken as the center to intercept the image containing the object as the roughly segmented image. Then, the improved Chan-Vese model is used to segment the roughly segmented image, so as to reduce the time consumption of Chan-Vese model segmentation. In addition, as Chan-Vese model can't fully segment the image of object whose color is diverse and mutable, we use the improved Chan-Vese model to segment the roughly segmented image. The internal pixels of image evolution contours were processed by K-means clustering, and the clustering center point values were obtained. The internal fitting values of Chan-Vese model were constructed by the clustering center point values and the image mean filtered intensity information, thereby improving the adaptability of Chan-Vese model for complex object image segmentation. In addition, rectangular Dirac function was used to replace regularized Dirac function in the energy function of Chan-Vese model, and the calculation of level set evolution equation could be limited to the zero level set so as to avoid the influence of the image background disturbance on the segmentation result. In this paper, the video processing with 50 frames of images shows that the time consumption of this method is only 15.25 s, the average value of Dice similarity coefficient is 0.852929, and the average value of Jaccard index is 0.74457. In summary, the object detection method proposed in this paper can accurately extract the object contour and has a high real-time performance. © 2018, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
关键词	Algorithms Biology Ecology Image enhancement Image processing Information filtering K-means clustering Object detection Object recognition Time domain analysis Video signal processing Background subtraction Chan-Vese model Intelligent monitoring systems Intensity inhomogeneity Level set evolution equations Object detection method Ochotona curzoniae Spatio temporal
DOI	10.11975/j.issn.1002-6819.2018.09.024
收录类别	EI
语种	中文
出版者	Chinese Society of Agricultural Engineering
EI入藏号	20182405309167
EI主题词	Image segmentation
EI分类号	454.3 Ecology and Ecosystems - 461.9 Biology - 716.4 Television Systems and Equipment - 723.2 Data Processing and Image Processing - 903.1 Information Sources and Analysis - 921 Mathematics
来源库	Compendex
分类代码	454.3 Ecology and Ecosystems - 461.9 Biology - 716.4 Television Systems and Equipment - 723.2 Data Processing and Image Processing - 903.1 Information Sources and Analysis - 921 Mathematics
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/113820
专题	电气工程与信息工程学院计算机与通信学院
作者单位	1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 3.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 4.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Aihua,Wang, Fan,Chen, Haiyan. Moving object detection of Ochotona curzoniae based on spatio-temporal imformation[J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,2018,34(9):197-203.
APA	Zhang, Aihua,Wang, Fan,&Chen, Haiyan.(2018).Moving object detection of Ochotona curzoniae based on spatio-temporal imformation.Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,34(9),197-203.
MLA	Zhang, Aihua,et al."Moving object detection of Ochotona curzoniae based on spatio-temporal imformation".Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 34.9(2018):197-203.