Drone-Person Tracking in Uniform Appearance Crowd (D-PTUAC)

Drone-person tracking in uniform appearance crowds poses unique challenges due to the difficulty in distinguishing individuals with similar attire and multi-scale variations. To address this issue and facilitate the development of effective tracking algorithms, we present a novel dataset named D-PTUAC (Drone-Person Tracking in Uniform Appearance Crowd). The dataset comprises 138 sequences comprising over 121K frames, each manually annotated with bounding boxes and attributes. During dataset creation, we carefully consider 17 challenging attributes encompassing a wide range of viewpoints and scene complexities. These attributes are annotated to facilitate the analysis of performance based on specific attributes. Extensive experiments are conducted using 44 state-of-the-art (SOTA) trackers, and the performance gap demonstrate the need for a dedicated end-to-end aerial visual object tracker that accounts the inherent properties of aerial environment.<br>

在着装统一的人群场景中实施无人机视角行人跟踪，面临独特挑战——不仅难以区分着装高度相似的个体，同时还存在多尺度变化带来的干扰。为解决该问题并推动高效跟踪算法的研发，我们构建了一款全新数据集，命名为D-PTUAC（着装统一人群无人机行人跟踪数据集，Drone-Person Tracking in Uniform Appearance Crowd）。该数据集包含138段视频序列，总计超过12.1万帧图像，每帧均经人工标注了边界框与属性信息。在数据集构建过程中，我们精心设计了17项具有挑战性的属性维度，涵盖多样的拍摄视角与复杂场景类型。上述属性均已完成标注，以支持基于特定属性的算法性能评估。我们采用44款当前最先进（State-of-the-Art，简称SOTA）的跟踪器开展了大量实验，实验所揭示的性能差距表明，亟需一款专门适配航拍场景固有特性的端到端航拍视觉目标跟踪器。

搜集汇总

背景与挑战

背景概述

D-PTUAC是一个针对无人机视角下穿着相似人群的人员追踪数据集，包含138个序列和超过121K帧的标注数据，旨在支持开发适应空中环境特性的追踪算法。