Robust classification of animal tracking data

Authors:
Mac Schwager;Dean M. Anderson;Zack Butler;Daniela Rus
Affiliations:
Distributed Robotics Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States;USDA-ARS, Jornada Experimental Range, Las Cruces, NM 88003, United States;Computer Science Department, Rochester Institute of Technology, Rochester, NY 14623, United States;Distributed Robotics Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
Venue:
Computers and Electronics in Agriculture
Year:
2007

Citing 1
Cited 10

Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet

Proceedings of the 10th international conference on Architectural support for programming languages and operating systems

ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass

Computers and Electronics in Agriculture
An automated sensor-based method of simple behavioural classification of sheep in extensive systems

Computers and Electronics in Agriculture
Towards collaborative data reduction in stream-processing systems

International Journal of Communication Networks and Distributed Systems
Observer Kalman filter identification and multiple-model adaptive estimation technique for classifying animal behaviour using wireless sensor networks

Computers and Electronics in Agriculture
PerPos: a platform providing cloud services for pervasive positioning

Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Original paper: ZigBee-based wireless sensor network localization for cattle monitoring in grazing fields

Computers and Electronics in Agriculture
Review: The evolution of virtual fences: A review

Computers and Electronics in Agriculture
High classification rates for continuous cow activity recognition using low-cost GPS positioning sensors and standard machine learning techniques

ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

Computers and Electronics in Agriculture
Automatic identification of marked pigs in a pen using image pattern recognition

Computers and Electronics in Agriculture

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.