# Meerkat Behaviour Recognition Dataset

arXiv: https://arxiv.org/abs/2306.11326 · HTML: https://mlsanity.com/paper/2306.11326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/1906.04477/) (2019)
- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Exploiting Depth Information for Wildlife Monitoring](https://mlsanity.com/paper/2102.05607/) (2021)

## Built on by (most cited first, 1 of 1)

- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)

## Abstract (arXiv)

Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviour within a zoo setting, such as scan sampling, require excessive human effort, are unfit for around-the-clock monitoring, and may produce human-biased results. Several animal datasets already exist that focus predominantly on wildlife interactions, with some extending to action or behaviour recognition. However, there is limited data in a zoo setting or data focusing on the group behaviours of social animals. We introduce a large meerkat (Suricata Suricatta) behaviour recognition video dataset with diverse annotated behaviours, including group social interactions, tracking of individuals within the camera view, skewed class distribution, and varying illumination conditions. This dataset includes videos from two positions within the meerkat enclosure at the Wellington Zoo (Wellington, New Zealand), with 848,400 annotated frames across 20 videos and 15 unannotated videos.