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The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline

文献类型：期刊论文

作者Yu, Hongyang2; Li, Guorong3; Zhang, Weigang4; Huang, Qingming2,3,6,7; Du, Dawei3; Tian, Qi1; Sebe, Nicu5
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2019-12-03
页码19
关键词UAV Object detection Single object tracking Multiple object tracking
ISSN号0920-5691
DOI10.1007/s11263-019-01266-1
英文摘要With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed.
资助项目National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61772494] ; National Natural Science Foundation of China[61976069] ; CAS[QYZDJ-SSW-SYS013] ; Italy-China collaboration project TALENT[2018YFE0118400] ; University of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; ARO[W911NF-15-1-0290] ; NEC Laboratory of America ; NEC Laboratory of Blippar
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000500723700001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/14940]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Guorong; Huang, Qingming
作者单位1.Univ Texas San Antonio, San Antonio, TX USA
2.Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Harbin Inst Technol, Weihai, Peoples R China
5.Univ Trento, Trento, Italy
6.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Comp, Key Lab Intelligent Informat Proc IIP, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Hongyang,Li, Guorong,Zhang, Weigang,et al. The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2019:19.
APA Yu, Hongyang.,Li, Guorong.,Zhang, Weigang.,Huang, Qingming.,Du, Dawei.,...&Sebe, Nicu.(2019).The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline.INTERNATIONAL JOURNAL OF COMPUTER VISION,19.
MLA Yu, Hongyang,et al."The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline".INTERNATIONAL JOURNAL OF COMPUTER VISION (2019):19.

入库方式： OAI收割

来源：计算技术研究所

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