As drone technology advances, using unmanned aerial vehicles for aerial
surveys has become the dominant trend in modern low-altitude remote sensing.
The surge in aerial video data necessitates accurate prediction for future
scenarios and motion states of the interested target, particularly in
applications like traffic management and disaster response. Existing video
prediction methods focus solely on predicting future scenes (video frames),
suffering from the neglect of explicitly modeling target's motion states, which
is crucial for aerial video interpretation. To address this issue, we introduce
a novel task called Target-Aware Aerial Video Prediction, aiming to
simultaneously predict future scenes and motion states of the target. Further,
we design a model specifically for this task, named TAFormer, which provides a
unified modeling approach for both video and target motion states.
Specifically, we introduce Spatiotemporal Attention (STA), which decouples the
learning of video dynamics into spatial static attention and temporal dynamic
attention, effectively modeling the scene appearance and motion. Additionally,
we design an Information Sharing Mechanism (ISM), which elegantly unifies the
modeling of video and target motion by facilitating information interaction
through two sets of messenger tokens. Moreover, to alleviate the difficulty of
distinguishing targets in blurry predictions, we introduce Target-Sensitive
Gaussian Loss (TSGL), enhancing the model's sensitivity to both target's
position and content. Extensive experiments on UAV123VP and VisDroneVP (derived
from single-object tracking datasets) demonstrate the exceptional performance
of TAFormer in target-aware video prediction, showcasing its adaptability to
the additional requirements of aerial video interpretation for target
awareness.

无人机技术的不断进步使得使用无人机进行高空遥感成为现代低空遥感的主导趋势。随着航拍视频数据的激增，对于感兴趣目标未来场景和运动状态的准确预测尤为重要，尤其在交通管理和灾难响应等应用领域。现有的视频预测方法仅关注预测未来场景，忽视了目标运动状态的显式建模，这对于航拍视频解释来说至关重要。针对该问题，我们引入了一项新颖的任务，命名为目标感知航拍视频预测，旨在同时预测未来场景和目标的运动状态。此外，我们设计了一个专门针对该任务的模型，名为 TAFormer，它提供了一个统一的建模方法，可以同时建模视频和目标的运动状态。具体而言，我们引入了时空关注机制（STA），将视频动态学习分解为空间静态关注和时间动态关注，有效建模场景外观和运动。另外，我们设计了一种信息共享机制（ISM），通过两组信使令牌促进信息交互，优雅地统一了对视频和目标运动的建模。另外，为了减轻模糊预测中目标识别的困难，我们引入了目标敏感高斯损失（TSGL），增强了模型对目标位置和内容的敏感性。在 UAV123VP 和 VisDroneVP 等数据集上进行了大量实验证明了 TAFormer 在目标感知视频预测方面的卓越性能，展示了其适应航拍视频解释的额外要求。