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TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes by Xu, Liangyu; Lu, Wanxuan; Yu, Hongfeng; Mao, Yongqiang; Bi, Hanbo; Liu, Chenglong; Sun, Xian; Fu, Kun is a scholarly article available to read on EtoBox.

What is TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes about?

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

Author

Xu, Liangyu; Lu, Wanxuan; Yu, Hongfeng; Mao, Yongqiang; Bi, Hanbo; Liu, Chenglong; Sun, Xian; Fu, Kun

Published

2024

Language

EN