In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform. The images and video sequences in the benchmark were captured over various urban/suburban areas of 14 different cities across China from north to south. Specifically, VisDrone2018 consists of 263 video clips and 10,209 images (no overlap with video clips) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. With intensive amount of effort, our benchmark has more than 2.5 million annotated instances in 179,264 images/video frames. Being the largest such dataset ever published, the benchmark enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. In particular, we design four popular tasks with the benchmark, including object detection in images, object detection in videos, single object tracking, and multi-object tracking. All these tasks are extremely challenging in the proposed dataset due to factors such as occlusion, large scale and pose variation, and fast motion. We hope the benchmark largely boost the research and development in visual analysis on drone platforms.

本篇论文介绍了一个名为VisDrone2018的大规模视觉对象检测和跟踪基准，目的在于推进基于无人机平台上的视觉理解任务，其包含了14个不同城市的各种城市/郊区地区的图像和视频序列，并提供了丰富的标注，如物体边界框的位置, 物体类别, 遮挡, 截断比等，是迄今为止发表的最大的数据集之一，可广泛评估并研究在无人机平台上的视觉分析技术。

视觉与无人机：一项挑战