Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs

Gao, Yang; Gan, Zhihong; Chen, Min; Ma, He; Mao, Xingpeng

doi:10.3390/drones8010003

Cited by 2 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual target tracking technology is not only widely used in civil fields such as security, logistics, and rescue but also plays an important role in military fields such as intelligence collection, guidance, and remote sensing [4]. However, object tracking technology from the UAV perspective faces several challenges and problems [5].…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

Sun,

Li,

Yang

et al. 2024

Drones

View full text Add to dashboard Cite

With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking from the UAV perspective experiences interference from various complex conditions such as background clutter, occlusion, and being out of view, which can easily lead to tracking drift. Once tracking drift occurs, it will lead to almost complete failure of the subsequent tracking. Currently, few trackers have been designed to solve the tracking drift problem. Thus, this paper proposes a tracking algorithm based on motion prediction and block search to address the tracking drift problem caused by various complex conditions. Specifically, when the tracker experiences tracking drift, we first use a Kalman filter to predict the motion state of the target, and then use a block search module to relocate the target. In addition, to improve the tracker’s ability to adapt to changes in the target’s appearance and the environment, we propose a dynamic template updating network (DTUN) that allows the tracker to make appropriate template decisions based on various tracking conditions. We also introduce three tracking evaluation metrics: namely, average peak correlation energy, size change ratio, and tracking score. They serve as prior information for tracking status identification in the DTUN and the block prediction module. Extensive experiments and comparisons with many competitive algorithms on five aerial benchmarks, UAV20L, UAV123, UAVDT, DTB70, and VisDrone2018-SOT, demonstrate that our method achieves significant performance improvements. Especially in UAV20L long-term tracking, our method outperforms the baseline in terms of success rate and accuracy by 19.1% and 20.8%, respectively. This demonstrates the superior performance of our method in the task of long-term tracking from the UAV perspective, and we achieve a real-time speed of 43 FPS.

show abstract

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

Sun,

Li,

Yang

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

UAV Hunter: A Net-Capturing UAV System with Improved Detection and Tracking Methods for Anti-UAV Defense

Zhang,

Lu,

Yang

et al. 2024

Drones

View full text Add to dashboard Cite

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs.

show abstract

Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs

Cited by 2 publications

References 36 publications

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

UAV Hunter: A Net-Capturing UAV System with Improved Detection and Tracking Methods for Anti-UAV Defense

Contact Info

Product

Resources

About