标题：VisDrone-SOT2018: The vision meets drone single-object tracking challenge results
作者：Wen, Longyin ;Zhu, Pengfei ;Du, Dawei ;Bian, Xiao ;Ling, Haibin ;Hu, Qinghua ;Liu, Chenfeng ;Cheng, Hao ;Liu, Xiaoyu ;Ma, Wenya ;Nie, Qinqin ;Wu, Haot 更多
作者机构：[Wen, L] JD Finance, Mountain View, CA, United States;[ Zhu, P] Tianjin University, Tianjin, China;[ Du, D] University at Albany, SUNY, Albany, NY, Un 更多
会议名称：15th European Conference on Computer Vision, ECCV 2018
会议日期：8 September 2018 through 14 September 2018
来源：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
关键词：Drone; Performance evaluation; Single-object tracking
摘要：Single-object tracking, also known as visual tracking, on the drone platform attracts much attention recently with various applications in computer vision, such as filming and surveillance. However, the lack of commonly accepted annotated datasets and standard evaluation platform prevent the developments of algorithms. To address this issue, the Vision Meets Drone Single-Object Tracking (VisDrone-SOT2018) Challenge workshop was organized in conjunction with the 15th European Conference on Computer Vision (ECCV 2018) to track and advance the technologies in such field. Specifically, we collect a dataset, including 132 video sequences divided into three non-overlapping sets, i.e., training (86 sequences with 69, 941 frames), validation (11 sequences with 7,046 frames), and testing (35 sequences with 29, 367 frames) sets. We provide fully annotated bounding boxes of the targets as well as several useful attributes, e.g., occlusion, background clutter, and camera motion. The tracking targets in these sequences include pedestrians, cars, buses, and animals. The dataset is extremely challenging due to various factors, such as occlusion, large scale, pose variation, and fast motion. We present the evaluation protocol of the VisDrone-SOT2018 challenge and the results of a comparison of 22 trackers on the benchmark dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/. We hope this challenge largely boosts the research and development in single object tracking on drone platforms. © Springer Nature Switzerland AG 2019.