Knowledge Commons of Institute of Automation,CAS
BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision | |
Xin Zhao1,2; Shiyu Hu2; Yipei Wang3; Zhang Jing2; Yimin Hu4; Rongshuai Liu4; Haibin Ling5; Yin Li6; Renshu Li4; Kun Liu4; Jiadong Li4 | |
发表期刊 | International Journal of Computer Vision |
2024 | |
卷号 | 132页码:1659-1684 |
摘要 | Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone—the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack Mayer et al. in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 13444–13454, 2021) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com. |
收录类别 | SCI |
七大方向——子方向分类 | 无人系统 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57469 |
专题 | 复杂系统认知与决策实验室_智能系统与工程 |
通讯作者 | Shiyu Hu |
作者单位 | 1.University of Science and Technology Beijing 2.Institute of Automation, Chinese Academy of Sciences 3.School of Instrument Science and Engineering, Southeast University, 4.Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences 5.Department of Computer Science, Stony Brook University 6.University of Wisconsin-Madison |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Xin Zhao,Shiyu Hu,Yipei Wang,et al. BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision[J]. International Journal of Computer Vision,2024,132:1659-1684. |
APA | Xin Zhao.,Shiyu Hu.,Yipei Wang.,Zhang Jing.,Yimin Hu.,...&Jiadong Li.(2024).BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision.International Journal of Computer Vision,132,1659-1684. |
MLA | Xin Zhao,et al."BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision".International Journal of Computer Vision 132(2024):1659-1684. |
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