Online Collective Animal Movement Activity Recognition

Kehinde Owoeye, Stephen Hailes

Keywords: agricultural monitoring and control, deep learning/convolutional LSTM, multi-sensor fusion, Collective animal behaviour recognition

TL;DR: Collective animal behaviour recognition

Abstract:

Learning the activities of animals is important for the purpose of monitoring their
welfare vis a vis their behaviour with respect to their environment and conspecifics.
While previous works have largely focused on activity recognition in a single
animal, little or no work has been done in learning the collective behaviour of
animals. In this work, we address the problem of recognising the collective
movement activities of a group of sheep in a flock. We present a discriminative
framework that learns to track the positions and velocities of all the animals in the
flock in an online manner whilst estimating their collective activity. We investigate
the performance of two simple deep network architectures and show that we can
learn the collective activities with good accuracy even when the distribution of the
activities is skewed.