Foreground Aware Correlation Filter with Adaptive Feature Response Fusion for Real-Time UAV Tracking
Zhuo Xiao, Yang Yi, Sixian Zhang, Wenbiao Li, Pengrong Bao, Deqiang Han · 2024
Background Aware Correlation Filter (BACF) tracker achieves accurate tracking result in visual object tracking by mitigating boundary effects, yet is limited in challenging scenarios especially in viewpoint change and illumination variation, which are frequently encountered in Unmanned Aerial Vehicle (UAV) tracking tasks. To address the shortcomings, we propose a Foreground Aware Correlation Filter with adaptive feature response fusion (FACF). In this paper, we use saliency detection to generate foreground prior knowledge in training phase for suppressing potential noise. Furthermore, recognizing the limitation of BACF, which relies on a single feature, a novel adaptive fusion strategy is designed to fuse multiple feature responses during the detection phase. This strategy aims to enhance the robustness of the tracker. Extensive experiments have been conducted on three challenging benchmarks. The tracking results show that the proposed tracker performs accurate and robust tracking result and satisfies real-time requirement with 48.28fps.