BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

TL;DR

This paper introduces BaboonLand, a comprehensive drone-based dataset for primate detection, tracking, and behavior recognition, enabling scalable, non-invasive analysis of wild primate groups in their natural habitat.

Contribution

It presents a novel, large-scale dataset from drone videos for primate detection, tracking, and behavior classification, with benchmark results demonstrating the effectiveness of deep learning models.

Findings

Detection mAP of 92.62% with YOLOv8-X

Tracking MOTA of 63.81% with BotSort

Behavior recognition accuracy of 63.97% with X3D

Abstract

Using drones to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding group primate behavior. Previous studies have demonstrated that it is possible to automate the classification of primate behavior from video data, but these studies have been carried out in captivity or from ground-based cameras. To understand group behavior and the self-organization of a collective, the whole troop needs to be seen at a scale where behavior can be seen in relation to the natural environment in which ecological decisions are made. This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. A tiling method was subsequently applied to create a pyramid of images at various scales from the original 5.3K resolution images, resulting in approximately 30K images used for baboon detection. The tracking dataset is derived from the detection dataset, where all bounding boxes are assigned the same ID throughout the video. This process resulted in half an hour of very dense tracking data. The behavior recognition dataset was generated by converting tracks into mini-scenes, a video subregion centered on each animal; each mini-scene was manually annotated with 12 distinct behavior types, resulting in over 20 hours of data. Benchmark results show mean average precision (mAP) of 92.62\% for the YOLOv8-X detection model, multiple object tracking precision (MOTA) of 63.81\% for the BotSort tracking algorithm, and micro top-1 accuracy of 63.97\% for the X3D behavior recognition model. Using deep learning to classify wildlife behavior from drone footage facilitates non-invasive insight into the collective behavior of an entire group.