short-paper

Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling

Authors:

Xiangzeng Zhou,

Yanning ZhangAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1163 - 1166

https://doi.org/10.1145/2733373.2806307

Published: 13 October 2015 Publication History

Abstract

Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.

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Cited By

Zhang SLiu WZhu YHan WHuang YLi J(2022)Research on fish identification in tropical waters under unconstrained environment based on transfer learningEarth Science Informatics10.1007/s12145-022-00783-x15:2(1155-1166)Online publication date: 2-Apr-2022
https://doi.org/10.1007/s12145-022-00783-x
Wang LZhang LWang JYi Z(2020)Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural NetworksIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2019.290050612:1(98-108)Online publication date: Mar-2020
https://doi.org/10.1109/TCDS.2019.2900506
Xin JDu XShi Y(2019)Classifier Adaptive Fusion: Deep Learning for Robust Outdoor Vehicle Visual TrackingIEEE Access10.1109/ACCESS.2019.29364337(118519-118529)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2936433
Show More Cited By

Index Terms

Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

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Information

Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Short-paper

Funding Sources

Foundation grant from NWPU
National Natural Science Foundation of China
Research Fund for the Doctoral Program of Higher Education of China

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Zhang SLiu WZhu YHan WHuang YLi J(2022)Research on fish identification in tropical waters under unconstrained environment based on transfer learningEarth Science Informatics10.1007/s12145-022-00783-x15:2(1155-1166)Online publication date: 2-Apr-2022
https://doi.org/10.1007/s12145-022-00783-x
Wang LZhang LWang JYi Z(2020)Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural NetworksIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2019.290050612:1(98-108)Online publication date: Mar-2020
https://doi.org/10.1109/TCDS.2019.2900506
Xin JDu XShi Y(2019)Classifier Adaptive Fusion: Deep Learning for Robust Outdoor Vehicle Visual TrackingIEEE Access10.1109/ACCESS.2019.29364337(118519-118529)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2936433
Xu XFang JLi QXie GXie JRen M(2019)Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification10.1007/978-981-13-7983-3_4(37-53)Online publication date: 28-Apr-2019
https://doi.org/10.1007/978-981-13-7983-3_4
Gao WZhang JLiu QJiang LWang YYin MZhou Y(2018)Binary Artificial Immune Algorithm for Adaptive Visual DetectionIEEE Access10.1109/ACCESS.2018.28698696(51587-51597)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2869869
Zhou XXie LZhang PZhang Y(2017)Online object tracking based on BLSTM-RNN with contextual-sequential labelingJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-017-0514-48:6(861-870)Online publication date: 14-Jun-2017
https://doi.org/10.1007/s12652-017-0514-4
Huang JYu ZCai ZGu ZCai ZGao WYu SDu Q(2017)Extreme learning machine with multi-scale local receptive fields for texture classificationMultidimensional Systems and Signal Processing10.1007/s11045-016-0414-328:3(995-1011)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s11045-016-0414-3

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