research-article

Aerial Object Tracking Dataset

Authors:

Hu Dashuai,

Mei Wei,

Feng XiaoyuAuthors Info & Claims

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

Pages 88 - 91

https://doi.org/10.1145/3239576.3239577

Published: 16 June 2018 Publication History

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Abstract

Visual object tracking is one of the most significant research areas in computer vision. Many trackers have been proposed and achieved great success in recent years. Accompanying these excellent trackers, many datasets of image sequences and methods for evaluating them have also been put forward. However, we can hardly find a set of sequences which is specially regard to air targets even if it is very vital in civil aviation as well as in military domain, especially in air defence tasks. On the account of this, a series of aerial object image sequences with different attributes have been collected from the internet and made out to dataset in the form of OTB-100 Sequences, which is a very famous dataset for generic target tracking.

References

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A. Yilmaz, O. Javed, and M. Shah. Object Tracking: A Survey. ACM Computing Surveys, 38(4):1--45, 2006.

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K. Cannons. A Review of Visual Tracking. Technical Report CSE-2008-07, York University, Canada, 2008.

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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei Fei. ImageNet: A Large Scale Hierarchical Image Database. In CVPR, 2009.

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Yi Wu, Jongwoo Lim, Ming-Hsuan Yang. Online Object Tracking: A Benchmark. In CVPR 2013.

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Yi Wu, Jongwoo Lim, Ming-Hsuan Yang. Object Tracking Benchmark. In PAMI 2015.

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Index Terms

Aerial Object Tracking Dataset
1. Information systems
  1. Data management systems
    1. Database design and models

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Published In

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

June 2018

261 pages

ISBN:9781450364607

DOI:10.1145/3239576

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2018

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ICAIP '18

ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing

June 16 - 18, 2018

Chengdu, China

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Index Terms

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BrackishMOT: The Brackish Multi-Object Tracking Dataset

A New Multi-Camera Dataset with Surveillance, Mobile and Stereo Cameras for Tracking, Situation Analysis and Crime Scene Investigation Applications

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