论文标题
检测和跟踪遇到无人机挑战
Detection and Tracking Meet Drones Challenge
论文作者
论文摘要
配备摄像头的无人机或无人机已在包括农业，航空摄影和监视在内的广泛应用中快速部署。因此，对从无人机收集的视觉数据的自动理解变得高度要求，从而使计算机视觉和无人机越来越紧密。为了促进和跟踪对象检测和跟踪算法的发展，我们与ECCV 2018，ICCV 2019和ECCV 2020结合了三个挑战研讨会，吸引了全球100多个团队。我们提供一个大规模的无人机捕获的数据集，Visdrone，其中包括四个轨道，即（1）图像对象检测，（2）视频对象检测，（3）单个对象跟踪和（4）多对象跟踪。在本文中，我们首先对对象检测和跟踪数据集和基准进行了详尽的审查，并讨论收集基于大规模无人机的对象检测和通过完全手动注释跟踪数据集的挑战。之后，我们描述了我们的Visdrone数据集，该数据集在中国从北到南的14个不同城市的各个城市/郊区捕获。 Visdrone是有史以来最大的此类数据集，可对无人机平台的视觉分析算法进行广泛的评估和研究。我们对无人机上的大规模对象检测和跟踪的当前状态进行详细分析，并结论挑战以及提出未来的方向。我们预计基准在无人机平台上的视频分析中会很大程度上促进研究和开发。所有数据集和实验结果都可以从https://github.com/visdrone/visdrone/visdrone-dataset下载。
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset.
