Continuous Marine Tracking via Autonomous UAV Handoff

2025-07-16

AI Summary

To address challenges in dynamic marine environments—including continuous real-time shark tracking, limited single-UAV endurance, and severe interference from lighting variations, occlusions, and sea conditions—this paper proposes a multi-UAV collaborative visual tracking system. Methodologically, we design a seamless visual handover protocol based on high-confidence feature matching to enable autonomous inter-UAV coordination and task transfer; integrate a stabilized RGB-D camera with an onboard computing unit; and deploy a customized OSTrack-based tracking pipeline supporting 100 Hz high-speed detection and tracking. Experimentally, our system achieves an 81.9% tracking success rate on a 5,200-frame shark dataset and maintains an 82.9% target coverage rate under handover-ready operation. These results significantly enhance robustness and spatial-temporal coverage for large-scale, long-duration marine tracking. To the best of our knowledge, this work presents the first real-world validation of sustained shark tracking enabled by multi-UAV vision-based handover under actual sea conditions.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks, in dynamic marine environments. The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline, enabling visual identification under challenging lighting, occlusion, and sea-state conditions. A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones, extending operational coverage beyond single-drone battery limitations. Performance is evaluated on a curated shark dataset of 5,200 frames, achieving a tracking success rate of 81.9% during real-time flight control at 100 Hz, and robustness to occlusion, illumination variation, and background clutter. We present a seamless UAV handoff framework, where target transfer is attempted via high-confidence feature matching, achieving 82.9% target coverage. These results confirm the viability of coordinated UAV operations for extended marine tracking and lay the groundwork for scalable, autonomous monitoring.

Problem

• Continuous real-time tracking of marine animals in dynamic environments
• Seamless transfer of tracking between drones to extend coverage
• Robust visual identification under challenging lighting and occlusion conditions

Innovation

• Autonomous UAV vision system for marine tracking
• Inter-UAV handoff protocol for extended coverage
• Custom-trained OSTrack pipeline for robust identification