Kavli Affiliate: Zheng Zhu
| First 5 Authors: Xue-Feng Zhu, Tianyang Xu, Jian Zhao, Jia-Wei Liu, Kai Wang
| Summary:
Unmanned Aerial Vehicles (UAVs) have been widely used in many areas,
including transportation, surveillance, and military. However, their potential
for safety and privacy violations is an increasing issue and highly limits
their broader applications, underscoring the critical importance of UAV
perception and defense (anti-UAV). Still, previous works have simplified such
an anti-UAV task as a tracking problem, where the prior information of UAVs is
always provided; such a scheme fails in real-world anti-UAV tasks (i.e. complex
scenes, indeterminate-appear and -reappear UAVs, and real-time UAV
surveillance). In this paper, we first formulate a new and practical anti-UAV
problem featuring the UAVs perception in complex scenes without prior UAVs
information. To benchmark such a challenging task, we propose the largest UAV
dataset dubbed AntiUAV600 and a new evaluation metric. The AntiUAV600 comprises
600 video sequences of challenging scenes with random, fast, and small-scale
UAVs, with over 723K thermal infrared frames densely annotated with bounding
boxes. Finally, we develop a novel anti-UAV approach via an evidential
collaboration of global UAVs detection and local UAVs tracking, which
effectively tackles the proposed problem and can serve as a strong baseline for
future research. Extensive experiments show our method outperforms SOTA
approaches and validate the ability of AntiUAV600 to enhance UAV perception
performance due to its large scale and complexity. Our dataset, pretrained
models, and source codes will be released publically.
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