Multi-Drone based Single Object Tracking with Agent Sharing Network
Pengfei Zhu, Jiayu Zheng, Dawei Du, Longyin Wen, Yiming Sun, Qinghua, Hu

TL;DR
This paper introduces a new multi-drone dataset and a novel agent sharing network that enhances single object tracking accuracy by leveraging multiple drones and self-supervised template sharing.
Contribution
The paper presents a new multi-drone dataset, specialized evaluation metrics, and a novel agent sharing network for improved multi-drone object tracking.
Findings
ASNet outperforms recent state-of-the-art trackers.
New MDOT dataset with high-resolution drone footage.
Designed metrics AFS and IFS for multi-drone tracking evaluation.
Abstract
Drone equipped with cameras can dynamically track the target in the air from a broader view compared with static cameras or moving sensors over the ground. However, it is still challenging to accurately track the target using a single drone due to several factors such as appearance variations and severe occlusions. In this paper, we collect a new Multi-Drone single Object Tracking (MDOT) dataset that consists of 92 groups of video clips with 113,918 high resolution frames taken by two drones and 63 groups of video clips with 145,875 high resolution frames taken by three drones. Besides, two evaluation metrics are specially designed for multi-drone single object tracking, i.e. automatic fusion score (AFS) and ideal fusion score (IFS). Moreover, an agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · UAV Applications and Optimization · Fire Detection and Safety Systems
