Video scene graph generation (VidSGG) has emerged as a transformative
approach to capturing and interpreting the intricate relationships among
objects and their temporal dynamics in video sequences. In this paper, we
introduce the new AeroEye dataset that focuses on multi-object relationship
modeling in aerial videos. Our AeroEye dataset features various drone scenes
and includes a visually comprehensive and precise collection of predicates that
capture the intricate relationships and spatial arrangements among objects. To
this end, we propose the novel Cyclic Graph Transformer (CYCLO) approach that
allows the model to capture both direct and long-range temporal dependencies by
continuously updating the history of interactions in a circular manner. The
proposed approach also allows one to handle sequences with inherent cyclical
patterns and process object relationships in the correct sequential order.
Therefore, it can effectively capture periodic and overlapping relationships
while minimizing information loss. The extensive experiments on the AeroEye
dataset demonstrate the effectiveness of the proposed CYCLO model,
demonstrating its potential to perform scene understanding on drone videos.
Finally, the CYCLO method consistently achieves State-of-the-Art (SOTA) results
on two in-the-wild scene graph generation benchmarks, i.e., PVSG and ASPIRe.

本文介绍了 AeroEye 数据集，该数据集专注于无人机视频中的多对象关系建模，并提出了一种名为 CYCLO 的新颖循环图变换模型，该模型能够在更新历史交互的同时捕捉直接和远程的时间依赖关系，并能够正确地处理对象关系的顺序，从而实现了对无人机视频的场景理解，并在两个实景图生成评估上取得了最先进的结果。

CYCLO: 高空视频中多物体关系建模的循环图变换方法

CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship  Modeling in Aerial Videos

In recent years, algorithms for multiple object tracking tasks have benefited
from great progresses in deep models and video quality. However, in challenging
scenarios like drone videos, they still suffer from problems, such as small
objects, camera movements and view changes. In this paper, we propose a new
multiple object tracker, which employs Global Information And some Optimizing
strategies, named GIAOTracker. It consists of three stages, i.e., online
tracking, global link and post-processing. Given detections in every frame, the
first stage generates reliable tracklets using information of camera motion,
object motion and object appearance. Then they are associated into trajectories
by exploiting global clues and refined through four post-processing methods.
With the effectiveness of the three stages, GIAOTracker achieves
state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place
in the VisDrone2021 MOT Challenge.

本研究提出了一个新的多目标追踪器 GIAOTracker，它通过三阶段的在线追踪、全局关联和后处理框架来解决无人机视频中小目标、相机运动和视角变化等挑战，达到了领先水平，并在 VisDrone2021 MOT 挑战中获得第三名。