Evolving Systems

, Volume 7, Issue 4, pp 277–285 | Cite as

Adaptive cow movement detection using evolving spiking neural network models

Original Paper

Abstract

For the need of automatic and intelligent dairy cow farming, it is important to combine more information technologies with the surveillance system. Different gestures may provide different healthy status information of the cow, in order to timely detect the abnormal activity and reduce the workload of breeder, a system which imitates the cognition of human brain is proposed in this paper. First, AdaBoost method is used to detect the position of cow in the surveillance video sequence, and then the 3D Evolving Spiking Neural Network Model is training to recognize different gestures and classify the activity in the real-time video surveillance. Experiment shows the average accuracy of detection and classification of the proposed system is about 80 %, and the performance is robust in complex natural environment. The proposed method can be used as the base of whole alert system which can help breeders to discover abnormal activity and to prevent the diseases in advance, so as to improve the welfare of dairy cow and the quality of milk.

Keywords

Diary cow farming Cow detection Gesture classification AdaBoost NeuCube Evolving Spiking Neural Network Video surveillance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of AutomationNorth China Electric Power UniversityBaodingChina
  2. 2.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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