Vision-based Anti-UAV Detection and Tracking

TL;DR

This paper introduces a new vision-based dataset and method for detecting and tracking UAVs using computer vision, demonstrating improved performance through detection and tracking fusion.

Contribution

The paper presents the DUT Anti-UAV dataset and a simple detection-based tracking algorithm, advancing UAV monitoring with publicly available data and improved methods.

Findings

Detection algorithms trained on DUT Anti-UAV perform well.

Fusing detection with tracking significantly improves UAV tracking accuracy.

The dataset and results are publicly accessible for further research.

Abstract

Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus we propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences. All frames and images are manually annotated precisely. We use this dataset to train several existing detection algorithms and evaluate the algorithms' performance. Several tracking methods are also tested on our tracking dataset. Furthermore, we propose a clear and simple tracking algorithm combined with detection that inherits the detector's high precision. Extensive experiments show that the tracking performance is improved considerably after fusing detection, thus providing a new attempt at UAV tracking using our dataset.The datasets and results are publicly available at: https://github.com/wangdongdut/DUT-Anti-UAV