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

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

748 Citations (Scopus)

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	29th IEEE Conference on Computer Vision and Pattern Recognition
Subtitle of host publication	proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4303-4311
Number of pages	9
ISBN (Electronic)	9781467388511, 9781467388504
ISBN (Print)	9781467388528
DOIs	https://doi.org/10.1109/CVPR.2016.466
Publication status	Published - 2016
Externally published	Yes
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
ISSN (Electronic)	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

@inproceedings{885a5b11bdcf4da59d2bf0ab4d8bd9dc,

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 Ming-Hsuan Yang",

year = "2016",

doi = "10.1109/CVPR.2016.466",

language = "English",

isbn = "9781467388528",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "4303--4311",

booktitle = "29th IEEE Conference on Computer Vision and Pattern Recognition",

address = "United States",

note = "29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016",

}

Qi, Y, Zhang, S, Qin, L, Yao, H, Huang, Q, Lim, J & Yang, M-H 2016, Hedged deep tracking. in 29th IEEE Conference on Computer Vision and Pattern Recognition: proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, 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

TY - GEN

T1 - Hedged deep tracking

AU - Qi, Yuankai

AU - Zhang, Shengping

AU - Qin, Lei

AU - Yao, Hongxun

AU - Huang, Qingming

AU - Lim, Jongwoo

AU - Yang, Ming-Hsuan

PY - 2016

Y1 - 2016

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.

UR - http://www.scopus.com/inward/record.url?scp=84986246054&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2016.466

DO - 10.1109/CVPR.2016.466

M3 - Conference proceeding contribution

AN - SCOPUS:84986246054

SN - 9781467388528

SP - 4303

EP - 4311

BT - 29th IEEE Conference on Computer Vision and Pattern Recognition

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

Y2 - 26 June 2016 through 1 July 2016

ER -

Hedged deep tracking

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