Robust classification of animal tracking data

被引：67

作者：

Schwager, Mac

Anderson, Dean M.

Butler, Zack

Rus, Daniela

机构：

[1] MIT, Distributed Robot Lab, Cambridge, MA 02139 USA

[2] USDA ARS, Jornada Expt Range, Las Cruces, NM 88003 USA

[3] Rochester Inst Technol, Dept Comp Sci, Rochester, NY 14623 USA

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2007年 / 56卷 / 01期

关键词：

cluster analysis; GPS; animal tracking; adaptive sampling; sensor networks;

D O I：

10.1016/j.compag.2007.01.002

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

This paper describes an application of the K-means classification algorithm to categorize animal tracking data into various classes of behavior. It was found that, even without explicit consideration of biological factors, the clustering algorithm repeatably resolved tracking data from cows into two groups corresponding to active and inactive periods. Furthermore, it is shown that this classification is robust to a large range of data sampling intervals. An adaptive data sampling algorithm is suggested for improving the efficiency of both energy and memory usage in animal tracking equipment. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：46 / 59

页数：14