无人机视觉挑战赛 | ICCV 2019 Workshop—VisDrone2019

2019 年 5 月 5 日 PaperWeekly
无人机视觉挑战赛 | ICCV 2019 Workshop—VisDrone2019


VisDrone 2019


The VisDrone 2019 Challenge will be held on the ICCV 2019 workshop "Vision Meets Drone: A Challenge" (or VisDrone2019, for short) in October, 2019, in Seoul, Korea, for object detection and tracking in visual data taken from drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining , Tianjin University, China. We invite researchers to participate the challenge and to evaluate and discuss their research at the workshop, as well as to submit papers describing research, experiments, or applications based on the VisDrone2019 dataset. 



14 different cities spanning thousands of kilometers

采集遍布中国14个城市


272117 video frames/images

272117张视频帧/图像


2.6 million bounding boxes

260万个标注框



     Four Tasks      

Task 1: object detection in images

The task aims to detect objects of predefined categories (e.g., cars and pedestrians) from individual images taken from drones. 

Task 2: object detection in videos 

The task is similar to Task 1, except that objects are required to be detected from videos. 

Task 3: single-object tracking

 The task aims to estimate the state of a target, indicated in the first frame, in the subsequent video frames. 

Task 4: multi-object tracking

The task aims to recover the trajectories of objects in each video frame.

Task1: object detection in images 


Task2: object detection in videos 


Task3: single-object tracking 


Task4: multi-object tracking 





Important Dates 


 Website open: April 25, 2019
 Data available: April 25, 2019
 Submission deadlineTBD
 Author notificationTBD

 Workshop dateTBD

 Camera-ready due: TBD





        Organizer         

Pengfei Zhu

Tianjin University 

Longyin Wen

JD Digits

Dawei Du

University AT Albany, SUNY 

Xiao Bian

GE Global Research


Qinghua Hu

Tianjin University

Haibin Ling

Temple University





Advisory Committee



  • Liefeng Bo (JD Digits, USA)

  • Hamilton Scott Clouse (US Airforce Research)

  • Liyi Dai (US Army Research Office)

  • Riad I. Hammound (BAE Systems, USA)

  • David Jacobs (Univ. Maryland College Park, USA)

  • SiweiLyu (Univ. At Albany, SUNY, USA)

  • Stan Z. Li (Institute of Automation, Chinese Academy of Sciences, China)

  • Fuxin Li (Oregon State Univ.,USA)

  • Anton Milan (Amazon Research and Development Center, Germany)

  • Hailin Shi (JD AI Research)

  • Siyu Tang (Max Planck Institute forIntelligent Systems, Germany)



Technical Committee



Hailin Shi

JD AI Research

Tao Peng

Tianjin University

Jiayu Zheng

Tianjin University



Yue Si

JD AI Research

Xiaolu Li

Tianjin University

Wenya Ma

Tianjin University




Sponsor 




Contact Us



Website:http://www.aiskyeye.com


Email:tju.drone.vision@gmail.com


WeChat Official Accounts



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Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.

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