Description of the 3rd Anti-UAV Workshop & Challenge

Civil unmanned aerial vehicles (UAVs), a.k.a. drones, have been widely used in a broad range of civil application domains, including consumer communications, delivery of goods, and remote sensing, owning to their autonomy, flexibility, affordability, and popularity. UAV applications offer possible civil and public domain applications in which single or multiple UAVs may be used. Nevertheless, we should be aware of the potential threat to our lives caused by UAV intrusion since UAVs can also be used to conduct physical attacks (e.g., via explosives) and cyber-attacks (e.g., hacking critical infrastructure). Moreover, unauthorized UAVs sometimes violate aviation safety regulations, thereby bringing hazards to civilian aircraft and passengers and even causing airport disruptions and flight delays. As shown in Fig. 1 and 2, there have been multiple instances of drone sightings halted air traffic at airports, leading to significant economic losses for airlines. It is highly desired to develop anti-UAV techniques to defend against drone accidents.

Historically, radar is certainly a compelling technology for detecting traditional incoming airborne threats. However, these comparatively small UAVs are extremely difficult for radar to see because they have very small radar cross-sections, low flight altitudes, and erratic flight paths. Therefore, how to use computer vision and machine learning algorithms to perceive UAVs is a crucial part of the whole UAV-defense system.

Traditional computer vision research for UAV detection and tracking lacks a high-quality benchmark in dynamic environments. To mitigate this gap, we held the 1-st Anti-UAV Workshop & Challenge with CVPR 2020, releasing a dataset consisting of 160 video sequences (both RGB and infrared). The workshop attracted attention from researchers all over the world. Many submitted solutions outperform the baseline method, making great contributions to addressing the anti-UAV problem. The 2-nd Anti-UAV Workshop & Challenge with ICCV 2021 extends the benchmark dataset to 250 high-quality, full HD thermal infrared video sequences, spanning multiple occurrences of multi-scale (i.e., large, small and tiny, as shown in Fig. 1) UAVs. The workshop encourages participants to develop automated methods that can detect and track UAVs in thermal infrared videos with high accuracy. Particularly, algorithms that can detect and track fast-moving drones in complex environments (e.g., occlusion by cloud/buildings/trees, and fake targets like kites, balloons, birds, etc.) are highly expected.

This workshop will bring together academic and industrial experts in the field of UAVs to discuss the techniques and applications of tracking UAVs. Participants are invited to submit their original contributions, surveys, and case studies that address the works of UAV’s detection and tracking issues.

Topics of interest

The submissions are expected to deal with visual perception and processing tasks which include but are not limited to:

• Applications of computer vision and machine learning on anti-UAV
• Strategies for searching UAVs based on RGB/NIR data
• Spectrum sensing techniques for UAVs detection
• Localization and open-set identification of UAVs
• Scene understanding for anti-UAVs
• Small/tiny object detection and tracking techniques
• Fine-grained UAV target recognition
• Real-time lightweight deep learning inference for anti-UAV
• Infrared image and video analysis for anti-UAV
• Large-scale benchmark datasets on anti-UAV
• Biologically inspired computer vision techniques for anti-UAV
• Technical survey on anti-UAV

Contact

Please feel free to send any question or comments to: zhaojian90@u.nus.edu, jinlei@bupt.edu.cn, lijianan@bit.edu.cn.

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