Continuous Marine Tracking via Autonomous UAV Handoff
- URL: http://arxiv.org/abs/2507.12763v1
- Date: Thu, 17 Jul 2025 03:35:53 GMT
- Title: Continuous Marine Tracking via Autonomous UAV Handoff
- Authors: Heegyeong Kim, Alice James, Avishkar Seth, Endrowednes Kuantama, Jane Williamson, Yimeng Feng, Richard Han,
- Abstract summary: This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks.<n>The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline.<n>A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones.
- Score: 0.4935992163749761
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
arXiv Detail & Related papers (2025-06-23T14:28:30Z) - WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs [4.215854427679142]
WildLive is a near real-time animal detection and tracking framework for high-resolution imagery running directly onboard aerial vehicles (UAVs)<n>The system performs multi-animal detection tracking at 17.81 fps for HD and 7.53 fps on 4K video streams suitable for operation during higher altitude flights.<n>Our dataset comprises 200K+ annotated animal instances across 19K+ frames from 4K UAV videos collected at the Ol Pejeta Conservancy in Kenya.
arXiv Detail & Related papers (2025-04-14T12:21:16Z) - A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.<n>We also propose a reinforcement learning-based drone tracking method called R-VAT.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - Improving Underwater Visual Tracking With a Large Scale Dataset and
Image Enhancement [70.2429155741593]
This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT)
It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles.
We propose a novel underwater image enhancement algorithm designed specifically to boost tracking quality.
The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers.
arXiv Detail & Related papers (2023-08-30T07:41:26Z) - AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility [125.77396380698639]
AVisT is a benchmark for visual tracking in diverse scenarios with adverse visibility.
AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios.
We benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes.
arXiv Detail & Related papers (2022-08-14T17:49:37Z) - R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of
Dynamic Scenes [69.6715406227469]
Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches.
We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
arXiv Detail & Related papers (2021-08-10T17:57:03Z) - A Multi-UAV System for Exploration and Target Finding in Cluttered and
GPS-Denied Environments [68.31522961125589]
We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles.
The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map.
Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
arXiv Detail & Related papers (2021-07-19T12:54:04Z) - Robust Autonomous Landing of UAV in Non-Cooperative Environments based
on Dynamic Time Camera-LiDAR Fusion [11.407952542799526]
We construct a UAV system equipped with low-cost LiDAR and binocular cameras to realize autonomous landing in non-cooperative environments.
Taking advantage of the non-repetitive scanning and high FOV coverage characteristics of LiDAR, we come up with a dynamic time depth completion algorithm.
Based on the depth map, the high-level terrain information such as slope, roughness, and the size of the safe area are derived.
arXiv Detail & Related papers (2020-11-27T14:47:02Z) - Distributed Variable-Baseline Stereo SLAM from two UAVs [17.513645771137178]
In this article, we employ two UAVs equipped with one monocular camera and one IMU each, to exploit their view overlap and relative distance measurements.
In order to control the glsuav agents autonomously, we propose a decentralized collaborative estimation scheme.
We demonstrate the effectiveness of the approach at high altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods.
arXiv Detail & Related papers (2020-09-10T12:16:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.