Multi-Object Detection and Tracking in Ant Colony Videos with Downstream Behavioral Analysis

Abstract

This thesis investigates the application and efficacy of the YOLO (You Only Look Once) framework for real-time detection and tracking of ants within colony videos. Specific challenges addressed include small object sizes, frequent occlusions, and dense interactions. Additionally, a user-friendly application was developed, enabling non-technical users to perform independent tracking and behavioral analyses utilizing the fine-tuned YOLO models. Model architecture, dataset preparation, optimization methods, multi-object tracking (MOT) integration, performance metrics, and downstream behavioral analyses are presented in detail, providing comprehensive insights into ant interactions and social structures. Results illustrate YOLO’s significant potential for ecological and eusocial behavioral research.