Proceedings ArticleDOI
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Dawei Du,Yue Zhang,Zexin Wang,Zhikang Wang,Zichen Song,Ziming Liu,Liefeng Bo,Hailin Shi,Rui Zhu,Aashish Kumar,Aijin Li,Almaz Zinollayev,Anuar Askergaliyev,Arne Schumann,Binjie Mao,Pengfei Zhu,Byeongwon Lee,Chang Liu,Changrui Chen,Chunhong Pan,Chunlei Huo,Da Yu,DeChun Cong,Dening Zeng,Dheeraj Reddy Pailla,Di Li,Longyin Wen,Dong Wang,Donghyeon Cho,Dongyu Zhang,Furui Bai,George Jose,Guangyu Gao,Guizhong Liu,Haitao Xiong,Hao Qi,Haoran Wang,Xiao Bian,Heqian Qiu,Hongliang Li,Huchuan Lu,Ildoo Kim,Jaekyum Kim,Jane Shen,Jihoon Lee,Jing Ge,Jingjing Xu,Jingkai Zhou,Haibin Lin,Jonas Meier,Jun Won Choi,Junhao Hu,Junyi Zhang,Junying Huang,Kaiqi Huang,Keyang Wang,Lars Sommer,Lei Jin,Lei Zhang,Qinghua Hu,Lianghua Huang,Lin Sun,Lucas Steinmann,Meixia Jia,Nuo Xu,Pengyi Zhang,Qiang Chen,Qingxuan Lv,Qiong Liu,Qishang Cheng,Tao Peng,Sai Saketh Chennamsetty,Shuhao Chen,Shuo Wei,Srinivas S S Kruthiventi,Sungeun Hong,Sungil Kang,Tong Wu,Tuo Feng,Varghese Alex Kollerathu,Wanqi Li,Jiayu Zheng,Wei Dai,Weida Qin,Weiyang Wang,Xiaorui Wang,Xiaoyu Chen,Xin Chen,Xin Sun,Xin Zhang,Xin Zhao,Xindi Zhang,Xinyao Wang,Xinyu Zhang,Xuankun Chen,Xudong Wei,Xuzhang Zhang,Yanchao Li,Yifu Chen,Yu Heng Toh,Yu Zhang,Yu Zhu,Yunxin Zhong +102 more
- pp 213-226
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TLDR
The Vision Meets Drone Object Detection in Image Challenge (VME-DET 2019) as discussed by the authors, held in conjunction with the 17th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones.Abstract:
Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.read more
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Posted Content
Vision Meets Drones: Past, Present and Future
TL;DR: The VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South, is described, being the largest such dataset ever published, and enables extensive evaluation and investigation of visual analysis algorithms on the drone platform.
Journal ArticleDOI
Deep learning-based object detection in low-altitude UAV datasets: A survey
TL;DR: A comprehensive review of the state of the art deep learning based object detection algorithms and analyze recent contributions of these algorithms to low altitude UAV datasets is provided.
Journal ArticleDOI
Detection and Tracking Meet Drones Challenge
TL;DR: VisDrone as discussed by the authors is a large-scale drone captured dataset, which includes four tracks, i.e., (1) image object detection, (2) video object detection and tracking, (3) single object tracking, and (4) multi-object tracking.
Journal ArticleDOI
Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors
Sasa Sambolek,Marina Ivašić-Kos +1 more
TL;DR: In this article, the reliability of existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade RCNN on a VisDrone benchmark and custom-made dataset SARD build to simulate rescue scenes was investigated.
Journal ArticleDOI
A Global-Local Self-Adaptive Network for Drone-View Object Detection
TL;DR: In this article, the authors propose an end-to-end global-local self-adaptive network (GLSAN) for drone-view object detection, which includes a global-layer detection network (GLDN), a simple yet efficient selfadaptive region selecting algorithm (SARSA), and a local super-resolution network (LSRN).
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