# The PanAf-SBR Dataset: Social Behaviour Recognition for Wild Great Apes

arXiv: https://arxiv.org/abs/2607.17399 · HTML: https://mlsanity.com/paper/2607.17399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [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)
- [ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors](https://mlsanity.com/paper/2310.16447/) (2023)
- [PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition](https://mlsanity.com/paper/2401.13554/) (2024)
- [Visual Recognition of Great Ape Behaviours in the Wild](https://mlsanity.com/paper/2011.10759/) (2020)
- [Triple-stream Deep Metric Learning of Great Ape Behavioural Actions](https://mlsanity.com/paper/2301.02642/) (2023)
- [BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos](https://mlsanity.com/paper/2405.17698/) (2024)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition](https://mlsanity.com/paper/2404.08937/) (2024)
- [AlphaChimp: Tracking and Behavior Recognition of Chimpanzees](https://mlsanity.com/paper/2410.17136/) (2024)
- [The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition](https://mlsanity.com/paper/2502.21201/) (2025)
- [PriVi: Towards A General-Purpose Video Model For Primate Behavior In The Wild](https://mlsanity.com/paper/2511.09675/) (2025)

## Abstract (arXiv)

Behavioural shifts in wild great ape populations, particularly the breakdown of social structures, can serve as an early indicator of population decline. Automating the detection of behaviours indicative of these shifts is therefore a critical task for conservation. Several valuable datasets have recently been introduced for the automated recognition of great ape behaviour, yet few include fine-grained social behaviour annotations, and those that do are captured either in captive settings or via aerial platforms such as UAVs. We address this gap by introducing PanAf-SBR, the first wild great ape camera trap dataset annotated with social behaviours. PanAf-SBR extends PanAf500 with 100 additional videos covering 36,063 frames. These come with 81,096 annotations including bounding boxes, segmentation masks, intra-video identities, and seven social behaviour classes defined under the action giver and receiver convention of ChimpACT. We use this data together with the AlphaChimp architecture to establish the first benchmarks for fine-grained social behaviour recognition in wild great apes from camera trap footage. We further conduct bidirectional transfer learning experiments between PanAf-SBR and the captive ChimpACT dataset, finding that cross-dataset pre-training is highly beneficial for specific classes rather than of uniform benefit. Finally, we examine the role of background context by inverting the segmentation masks to suppress non-ape pixels.