BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos
Isla Duporge, Maksim Kholiavchenko, Roi Harel, Scott Wolf, Dan, Rubenstein, Meg Crofoot, Tanya Berger-Wolf, Stephen Lee, Julie Barreau, Jenna, Kline, Michelle Ramirez, Charles Stewart

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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification
