# Online Collective Animal Movement Activity Recognition

arXiv: https://arxiv.org/abs/1811.09067 · HTML: https://mlsanity.com/paper/1811.09067/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming](https://mlsanity.com/paper/2312.08764/) (2023)

## Abstract (arXiv)

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.