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>在着装统一的人群中开展无人机载人员跟踪任务，面临着独特的挑战：难以区分着装相似的个体，同时还存在多尺度变化问题。为解决上述难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人员跟踪数据集，Drone-Person Tracking in Uniform Appearance Crowd）。该数据集包含138个序列，总计超过12.1万帧图像，所有帧均由人工标注边界框与属性信息。在数据集构建过程中，我们精心纳入了17项挑战性属性，覆盖多样化的拍摄视角与场景复杂度，标注这些属性旨在支持基于特定属性的跟踪算法性能分析。我们采用44款当前最优（SOTA）跟踪器开展了大量实验，实验结果所体现的性能差距表明，亟需一款适配空中场景固有特性的专用端到端空中视觉目标跟踪器。