.png){kind=icon}
返回
BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-023-01937-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robust vision
Visual tracking
Flapping-wing aerial vehicle
High-quality benchmark
Tracking evaluation system
期刊
IF:
9.3
论文数:
4.0K
被引数:
2.8W
机构
引用论文
Sustaining the efficiency of the Fe(0)/H2O system for Cr(VI) removal by MnO2 amendment.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏分享收藏AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning PipelineAutoMorph: 通过深度学习管道自动量化视网膜血管形态
OAAI
OAAI分享收藏Cobalt oxides nanoparticles supported on nitrogen-doped carbon nanotubes as high-efficiency cathode catalysts for microbial fuel cells负载在氮掺杂碳纳米管上的钴氧化物纳米颗粒作为微生物燃料电池的高效阴极催化剂
OAAI
OAAI分享收藏Optimal design of a manufacturing-remanufacturing-transport system within a reverse logistics chain
PREAI
分享收藏A critical review on various remediation approaches for heavy metal contaminants removal from contaminated soilsChemosphere
ChemosphereIF0
PREAI
分享收藏
查看更多内容