Hedged Deep Tracking

Yuankai Qi; Shengping Zhang; Lei Qin; Hongxun Yao; Qingming Huang; Jongwoo Lim; Ming Hsuan Yang

doi:10.1109/CVPR.2016.466

Hedged Deep Tracking

Yuankai Qi
, Shengping Zhang
, Lei Qin
, Hongxun Yao
, Qingming Huang
, Jongwoo Lim
, Ming Hsuan Yang

Harbin Institute of Technology
Chinese Academy of Sciences
University of Chinese Academy of Sciences
Hanyang University
University of California Merced

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

777 Scopus citations

Abstract

In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.

Original language	English
Title of host publication	Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Publisher	IEEE Computer Society
Pages	4303-4311
Number of pages	9
ISBN (Electronic)	9781467388504
DOIs	https://doi.org/10.1109/CVPR.2016.466
State	Published - 9 Dec 2016
Event	29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 26 Jun 2016 → 1 Jul 2016

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2016-December
ISSN (Print)	1063-6919

Conference

Conference	29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country/Territory	United States
City	Las Vegas
Period	26/06/16 → 1/07/16

Access to Document

10.1109/CVPR.2016.466

Cite this

Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., & Yang, M. H. (2016). Hedged Deep Tracking. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 4303-4311). Article 7780835 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.466

@inproceedings{a84ee0d298ad4d0f96d937b6bb2eabe4,

title = "Hedged Deep Tracking",

abstract = "In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.",

author = "Yuankai Qi and Shengping Zhang and Lei Qin and Hongxun Yao and Qingming Huang and Jongwoo Lim and Yang, \{Ming Hsuan\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016",

year = "2016",

month = dec,

day = "9",

doi = "10.1109/CVPR.2016.466",

language = "英语",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016",

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}

Qi, Y, Zhang, S, Qin, L, Yao, H, Huang, Q, Lim, J & Yang, MH 2016, Hedged Deep Tracking. in Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016., 7780835, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, IEEE Computer Society, pp. 4303-4311, 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, United States, 26/06/16. https://doi.org/10.1109/CVPR.2016.466

Hedged Deep Tracking. / Qi, Yuankai; Zhang, Shengping; Qin, Lei et al.
Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, 2016. p. 4303-4311 7780835 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hedged Deep Tracking

AU - Qi, Yuankai

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AU - Qin, Lei

AU - Yao, Hongxun

AU - Huang, Qingming

AU - Lim, Jongwoo

AU - Yang, Ming Hsuan

PY - 2016/12/9

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N2 - In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.

AB - In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.

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PB - IEEE Computer Society

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Y2 - 26 June 2016 through 1 July 2016

ER -

Hedged Deep Tracking

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