自然条件下猪只运动参量提取算法

doi:10.11772/j.issn.1001-9081.2016.10.2900

计算机应用 ›› 2016, Vol. 36 ›› Issue (10): 2900-2906.DOI: 10.11772/j.issn.1001-9081.2016.10.2900

自然条件下猪只运动参量提取算法

冯爱晶, 肖德琴

华南农业大学数学与信息学院, 广州 510642

收稿日期:2016-04-29 修回日期:2016-06-19 发布日期:2016-10-10
通讯作者: 肖德琴,E-mail:deqinx@scau.edu.cn
作者简介:冯爱晶(1991—),女,广东阳江人,硕士研究生,CCF会员,主要研究方向:计算机视觉;肖德琴(1970—),女,重庆人,教授,博士生导师,博士,CCF会员,主要研究方向:无线传感器网络、物联网。
基金资助:
广东省科技计划项目（2015A020209149）。

Motion parameter extraction algorithm for pigs under natural conditions

FENG Aijing, XIAO Deqin

College of Mathematics and Informatics, South China Agricultural University, Guangzhou Guangdong 510642, China

Received:2016-04-29 Revised:2016-06-19 Published:2016-10-10
Supported by:
BackgroundThis work is partially supported by the Science and Technology Project of Guangdong Province (2015A020209149).

摘要/Abstract

摘要： 自然条件下猪只日常运动时间、距离、速度、加速度等构成的运动数据，可作为猪只健康状况分析和遗传性能评估的重要数据基础。针对传感器采集猪只运动数据会给动物带来不舒适的问题，结合视频监控技术和数字图像处理技术，提出了一套能够应用于自然条件下的猪只识别与跟踪方法；并立足猪只日常，设计了基于最短距离匹配法的状态参量和运动位移、运动速度、运动加速度和运动角速度4个运动参量的提取算法。对自然条件下猪场实时视频8次实验的识别结果进行分析，结果表明猪只识别与跟踪算法能够抵抗轻度粘连和光照变化带来的影响，对现实猪场情景具有较好的处理效果；运动参量提取算法提取出的运动位移累加量、运动速度、运动加速度和运动角速度各时刻的具体数值，总体趋势上能够反映猪只活动，可为未来猪只行为研究提供数据基础。

关键词: 计算机视觉, 视频追踪, 运动参量, 运动分析, 自然条件

Abstract: The daily movement data of pigs, including exercise time, distance, speed, acceleration and so on, can be used as an important data base for the analysis and evaluation of the health status of pigs. Collecting movement data of pigs by sensors would make animals uncomfortable. Combined with video surveillance and digital image technology, a methodology for detecting and tracking pigs under natural conditions was put forward. Then state parameter and motion parameters of displacement, velocity, acceleration and angular velocity were extracted by shortest distance matching algorithm. Eight experiments in real-time monitoring of farms were given and analyzed. Experimental results show that the proposed method has good performance in real farm scene with adaption to mild adhesion and illumination change. In addition, the accumulated value of displacement, as well as velocity, acceleration and angular velocity of motion extracted from the videos, can reflect the movement of pigs in the overall trend and provide the data foundation for future research of pig behavior.

Key words: computer vision, video tracking, motion parameter, motion analysis, national condition

中图分类号:

TP399

冯爱晶, 肖德琴. 自然条件下猪只运动参量提取算法[J]. 计算机应用, 2016, 36(10): 2900-2906.

FENG Aijing, XIAO Deqin. Motion parameter extraction algorithm for pigs under natural conditions[J]. Journal of Computer Applications, 2016, 36(10): 2900-2906.

参考文献

[1] 林英禄. 运动数据的实时监测与健康评价模型的研究[D]. 上海:华东师范大学, 2015:1-85.(LIN Y L. Real-time sports data monitoring and research of fitness evaluation model[D]. Shanghai: East China Normal University, 2015:1-85.)
[2] 蔡美玲. 三维人体运动分析与动作识别方法[D]. 长沙:中南大学, 2013:1-137.(CAI M L. 3D human motion analysis and action recognition[D]. Changsha: Central South University, 2013:1-137.)
[3] ZHU W X, ZHOU J J, WU Y. Gait abnormality detected of pigs based on machine vision[J]. Applied Mechanics and Materials, 2014, 614:432-435.
[4] OCZAK M, ISMAYILOVA G, VIAZZI S. Analysis of aggressive behaviours of pigs by automatic video recordings[J]. Computers and Electronics in Agriculture, 2013, 99:209-217.
[5] VIAZZI S, ISMAYILOVA G, OCZAK M. Image feature extraction for classification of aggressive interactions among pigs[J]. Computers and Electronics in Agriculture, 2014, 104:57-62.
[6] 杨威, 俞守华. 视频监控技术在生猪规模化养殖中的应用[J]. 现代农业装备, 2014(4):37-41.(YANG W, YU S H. Video surveillance technology in large-scale pig farming[J]. Modern Agricultural Equipments, 2014(4):37-41.)
[7] 高云.基于无线传感器网络的猪运动行为监测系统研究[D]. 武汉:华中农业大学, 2014:1-174.(GAO Y. Study of pig behavior monitoring system based on WSN[D]. Wuhan: Huazhong Agricultural University, 2014:1-174.)
[8] 刘龙申, 沈明霞, 姚文, 等. 基于加速度传感器的母猪产前行为特征采集与分析[J]. 农业机械学报, 2013, 44(3):192-196, 191.(LIU L S, SHEN M X, YAO W, et al. Acquisition and analysis of sows' behavior before farrowing based on acceleration sensor[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(3):192-196, 191.)
[9] 尹令, 刘财兴, 洪添胜, 等. 基于无线传感器网络的奶牛行为特征监测系统设计[J]. 农业工程学报, 2010, 26(3): 203-208, 388.(YIN L, LIU C X, HONG T S, et al. Design of system for monitoring dairy cattle's behavioral features based on wireless sensor networks[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(3): 203-208, 388.)
[10] YU S H, CHEN J H, OU J Y, et al. Multi-target tracking based on regional correlation and color histogram match [J]. Advanced Materials Research, 2014, 998/999: 631-637.
[11] WEI X Q, JIA K, LAN J H. Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot [J]. Optik — International Journal for Light and Electron Optics, 2014, 125(19):5684-5689.
[12] 周丽萍, 陈志, 陈达, 等. 基于改进Otsu算法的生猪热红外图像耳根特征区域检测 [J]. 农业机械学报, 2016, 47(4):228-232.(ZHOU L P, CHEN Z, CHEN D, et al. Pig ear root detection based on adapted Otsu[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(4):228-232.)
[13] KASHIHA M A, BAHR C, OTT S. Automatic monitoring of pig locomotion using image analysis[J]. Livestock Science, 2014, 159:141-148.
[14] 宋浩然, 廖文帅, 赵一鸣. 基于加速度传感器ADXL330的高精度计步器[J]. 传感技术学报, 2006, 19(4):1005-1008.(SONG H R, LIAO W S, ZHAO Y M. Using 3-axis accelerometer ADXL330 to high accuracy pedometer[J]. Chinese Journal of Sensors and Actuators, 2006, 19(4):1005-1008.)

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[13]	张毅刘钰然罗元. 基于视觉的手势识别方法及其在数字信号处理器上的实现[J]. 计算机应用, 2014, 34(3): 833-836.
[14]	李冬睿李梅. 基于潜在狄利克雷分配的图像多层视觉表示方法[J]. 计算机应用, 2013, 33(08): 2310-2312.
[15]	封泽希张辉谢永明朱敏. 基于4目阵列的计算机视觉三维重建算法[J]. 计算机应用, 2011, 31(04): 1043-1046.

自然条件下猪只运动参量提取算法

Motion parameter extraction algorithm for pigs under natural conditions

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