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Vision Meets Drones: Past, Present and Future
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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.Abstract:
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the evelopments of object detection and tracking algorithms, we have organized two challenge workshops in conjunction with ECCV 2018, and ICCV 2019, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first presents a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from the website: this https URL.read more
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Journal ArticleDOI
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Jonathon Luiten,Aljosa Osep,Patrick Dendorfer,Philip H. S. Torr,Andreas Geiger,Laura Leal-Taixé,Bastian Leibe +6 more
TL;DR: This work presents a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers.
Journal ArticleDOI
HOTA: A Higher Order Metric for Evaluating Multi-object Tracking.
Jonathon Luiten,Aljosa Osep,Patrick Dendorfer,Philip H. S. Torr,Andreas Geiger,Andreas Geiger,Laura Leal-Taixé,Bastian Leibe +7 more
TL;DR: Higher order tracking accuracy (HOTA) as mentioned in this paper is proposed to explicitly balance the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers, which is able to capture important aspects of MOT performance not previously taken into account by established metrics.
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Posted ContentDOI
Deep Learning for UAV-based Object Detection and Tracking: A Survey
TL;DR: A comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods can be found in this article, where the authors outline the challenges, statistics of existing methods, and provide solutions from the perspectives of deep learning-based models in three research topics: object detection from the image and video, and object tracking from the video.
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.
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