论文标题
使用三发相关过滤器的无人飞行器进行稳健的视觉跟踪
Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters
论文作者
论文摘要
近年来，对象跟踪已广泛应用于无人机（UAV）任务。但是，现有算法仍然面临着诸如部分遮挡，混乱背景和其他具有挑战性的视觉因素等困难。受到尖端注意机制的启发，提出了一个新颖的对象跟踪框架来利用多层次的视觉关注。三个主要注意力，即情境关注，维度注意力和时空注意力，都集成到基于相关滤波器的跟踪管道的训练和检测阶段中。因此，所提出的跟踪器配备了针对具有挑战性因素的强大判别能力，同时在无人机方案中保持高运营效率。具有173个具有挑战性的无人机视频序列的两个知名基准的定量和定性实验证明了该框架的有效性。拟议的跟踪算法优于表现12种最新方法，在UAVDT的相对增益为4.8％，而UAV123@10FPS的相对增益为基线跟踪器，同时以$ \ sim $ \ sim $ 28帧的速度运行。
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the proposed tracker is equipped with robust discriminative power against challenging factors while maintaining high operational efficiency in UAV scenarios. Quantitative and qualitative experiments on two well-known benchmarks with 173 challenging UAV video sequences demonstrate the effectiveness of the proposed framework. The proposed tracking algorithm favorably outperforms 12 state-of-the-art methods, yielding 4.8% relative gain in UAVDT and 8.2% relative gain in UAV123@10fps against the baseline tracker while operating at the speed of $\sim$ 28 frames per second.
