Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking

TL;DR

This paper introduces Anti-UAV, a large multi-modal dataset for UAV tracking, along with a novel dual-flow semantic consistency method that enhances tracking robustness and accuracy.

Contribution

It provides the first large-scale UAV tracking dataset and proposes a new tracking approach leveraging semantic flow for improved performance.

Findings

Anti-UAV dataset contains over 580k annotated bounding boxes.

The proposed DFSC method improves tracking accuracy on the Anti-UAV benchmark.

Anti-UAV is a challenging dataset that pushes UAV tracking research forward.

Abstract

Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work, we consider the task of tracking UAVs, providing rich information such as location and trajectory. To facilitate research on this topic, we propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes. The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs. Furthermore, the advancement of addressing research challenges in Anti-UAV can help the design of anti-UAV systems, leading to better surveillance of UAVs. Besides, a novel approach named dual-flow semantic consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow across video sequences, the tracker learns more robust class-level semantic information and obtains more discriminative instance-level features. Experimental results demonstrate that Anti-UAV is very challenging, and the proposed method can effectively improve the tracker's performance. The Anti-UAV benchmark and the code of the proposed approach will be publicly available at https://github.com/ucas-vg/Anti-UAV.