Bearded Dragon Activity Recognition Pipeline: An AI-Based Approach to Behavioural Monitoring

TL;DR

This paper presents an AI-based system using YOLO models for real-time detection of basking and hunting behaviors in bearded dragons, improving behavioral monitoring efficiency.

Contribution

The study develops and evaluates a YOLO-based pipeline for automated reptile behavior recognition, with the selection of YOLOv8s for optimal performance.

Findings

YOLOv8s achieved high accuracy (mAP@0.5:0.95 = 0.855) for basking detection.

Hunting detection was less accurate due to weak cricket detection (mAP@0.5 = 0.392).

The system enables scalable, real-time behavioral monitoring in controlled environments.

Abstract

Traditional monitoring of bearded dragon (Pogona Viticeps) behaviour is time-consuming and prone to errors. This project introduces an automated system for real-time video analysis, using You Only Look Once (YOLO) object detection models to identify two key behaviours: basking and hunting. We trained five YOLO variants (v5, v7, v8, v11, v12) on a custom, publicly available dataset of 1200 images, encompassing bearded dragons (600), heating lamps (500), and crickets (100). YOLOv8s was selected as the optimal model due to its superior balance of accuracy (mAP@0.5:0.95 = 0.855) and speed. The system processes video footage by extracting per-frame object coordinates, applying temporal interpolation for continuity, and using rule-based logic to classify specific behaviours. Basking detection proved reliable. However, hunting detection was less accurate, primarily due to weak cricket detection (mAP@0.5 = 0.392). Future improvements will focus on enhancing cricket detection through expanded datasets or specialised small-object detectors. This automated system offers a scalable solution for monitoring reptile behaviour in controlled environments, significantly improving research efficiency and data quality.