基于视觉感知的鱼群运动行为特征参数提取

doi:10.3969/j.issn.1000-1158.2017.02.11

Abstract
Figure/Table
References (15)
Related Citation (15)

Download: PDF (451 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Against how to use biomonitoring technology to improve the recognition rate of abnormal water quality, the evaluation factors of biological water quality is studied and an improved algorithm of fish center is proposed. On the basis of this algorithm, it introduced new behavior characters of fish movement, such as fish dispersion, curvature and vicinity feature, carried out pretreatment for these characteristic parameters, established databases for evaluation factors of abnormal water quality and took the evaluation factors as inputs of support vector machine (SVM) to identify abnormal water quality. The results of experiment show that by introducing characteristic parameters for evaluation of abnormal water quality, its identification effect is better than other methods and the recognition rate is over 92%.

Key words： metrology biomonitoring algorithm of fish center evaluation factors support vector machine

Received: 11 January 2016 Published: 28 February 2017

PACS:	TB99
	TB96

Corresponding Authors: CHENG Shuhong E-mail: shhcheng@ysu.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	CHENG Shu-hong
	LI Lei-hua
	LIU Jie
	QIAN Lei

Cite this article:

CHENG Shu-hong,LI Lei-hua,LIU Jie, et al. Fish Movement Behavior Characteristic Parameter Extraction Based on Visual Perception[J]. Acta Metrologica Sinica, 2017, 38(2): 175-178.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2017.02.11 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2017/V38/I2/175

［1］何康信, 周启星. 污染胁迫下的分子生物标志物和分子诊断技术［J］. 农业工程学报, 2013, 29(7): 1-16.
［2］Noble R R P, Stalker L, Wakelin S A, et al. Biological monitoring for carbon capture and storage—A review and potential future developments［J］. International Journal of Greenhouse Gas Control, 2012, 10(5): 520-535.
［3］邵明武,丁中华. 水质监测中有机污染物标准物质的现状［J］. 计量学报, 2009, 30(5): 482-485.
［4］Lai C L, Chiu C L. Using image processing technology for water quality monitoring system［C］ //Machine Learning and Cybernetics (ICMLC), 2011 International Conference on. IEEE, 2011, 4: 1856-1861.
［5］Kuklina I,Kouba A,Kozák P. Real-time monitoring of water quality using fish and crayfish as bio-indicators: a review［J］. Environmental Monitoring and Assessment, 2013, 185(6): 5043-5053.

［6］Ma H, Tsai T F, Liu C C. Real-time monitoring of water quality using temporal trajectory of live fish［J］. Expert Systems with Applications, 2010, 37(7): 5158-5171.
［7］黄毅, 张金松, 韩小波, 等. 斑马鱼群体行为变化用于水质在线预警的研究［J］. 环境科学学报, 2014, 34(2): 398-403.
［8］Thida M, Eng H L, Chew B F. Automatic Analysis of Fish Behaviors and Abnormality Detection\[J\]. Transactions of the Japan Society of Mechanical Engineers A, 2009, 67(3):317-335.
［9］Liao Y, Xu J, Wang W. A method of water quality assessment based on biomonitoring and multiclass support vector machine［J］. Procedia Environmental Sciences, 2011, 10(10): 451-457.
［10］He X. Coefficient of variation and its application to strength prediction of clinched joints［J］. Advanced Science Letters, 2011, 4(4/5): 1757-1760.
［11］Kim C M, Shin M W, Jeong S M, et al. Real-time motion generating method for artifical fish［J］. Computer Science and Network Security, 2007, 7(10): 52-61.
［12］Jeon W, Kang S H, Leem J B, et al. Characterization of fish schooling behavior with different numbers of Medaka (Oryzias latipes) and goldfish (Carassius auratus) using a Hidden Markov Model［J］. Physica A Statistical Mechanics & Its Applications, 2013, 392(10): 2426-2433.
［13］Kato S, Nakagawa T, Ohkawa M, et al. A computer image processing system for quanti cation of zebrafish behavior［J］. Journal of Neuroscience Methods, 2004, 134(1): 1-7.
［14］程淑红, 刘洁, 李雷华. 基于鱼类运动行为的水质异常评价因子研究［J］. 仪器仪表学报, 2015, 36(8): 1759-1766.
［15］陈果, 周伽. 小样本数据的支持向量机回归模型参数及预测区间研究［J］. 计量学报, 2008, 29(1): 92-96.