Fisher Pruning for Developing Real-Time UAV Trackers

Unmanned aerial vehicle (UAV)-based tracking has shown large potential in various domains such as transportation, logistics, public safety, and more. However, deploying deep learning (DL)-based tracking algorithms on UAVs is challenging because of limitations in computing resources, battery capacity, and maximum load. Discriminative correlation filter (DCF)-based trackers have become a popular choice in the UAV tracking community owing to their ability to provide superior efficiency while consuming fewer resources. However, the limited representation learning ability of DCF-based trackers leads to lower precision in complex scenarios compared to DL-based methods. Filter pruning is a prevalent practice for deploying deep neural networks on edge devices with constrained resources, and it may be an effective way to solve problems encountered when deploying deep learning trackers on UAVs. However, the application of filter pruning to UAV tracking is underexplored, and a straightforward and useful pruning standard is desirable. This paper proposes using Fisher pruning to reduce the SiamFC++ model for UAV tracking, resulting in the F-SiamFC++ tracker. The proposed tracker achieves a remarkable balance between precision and efficiency, as demonstrated through exhaustive experiments on four popular UAV benchmarks: UAVDT, DTB70, UAV123@10fps, and Vistrone2018, showing state-of-the-art performance.