Online Collective Animal Movement Activity Recognition
This work addresses a gap in animal activity recognition by focusing on collective behavior, which is important for monitoring animal welfare in groups, though it is incremental as it applies existing deep network architectures to a new domain.
The paper tackles the problem of recognizing collective movement activities in a group of sheep, presenting a discriminative framework that learns to track positions and velocities online while estimating activities, achieving good accuracy even with skewed activity distributions.
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.