Article

Real-time compressive tracking

Authors:

Ming-Hsuan YangAuthors Info & Claims

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

Pages 864 - 877

https://doi.org/10.1007/978-3-642-33712-3_62

Published: 07 October 2012 Publication History

Abstract

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.

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Cited By

Wang ZHuang JChen SZhang ZCai Z(2022)Target Tracking Algorithm Based on Multi-feature Adaptive Fusion in Complex ScenesProceedings of the 2022 2nd International Conference on Control and Intelligent Robotics10.1145/3548608.3559213(310-315)Online publication date: 24-Jun-2022
https://dl.acm.org/doi/10.1145/3548608.3559213
Liang YWang DChen YXiao LLiu C(2021)Robust Visual Tracking Based on Convolutional Sparse CodingWireless Communications & Mobile Computing10.1155/2021/55312222021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5531222
Lu XChen SZheng ZWeng CXu R(2021)Generalized Intersection over Union Based Online Weighted Multiple Instance Learning Algorithm for Object TrackingProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457698(111-116)Online publication date: 26-Feb-2021
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Index Terms

Real-time compressive tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

October 2012

880 pages

ISBN:9783642337116

Editors:
Andrew Fitzgibbon
Microsoft Research Ltd., Cambridge, UK
,
Svetlana Lazebnik
Dept. of Computer Science, University of North Carolina, Chapel Hill, NC, UK
,
Pietro Perona
Dept. of Computer Science, California Institute of Technology, Pasadena, CA, UK
,
Yoichi Sato
Institute of Industrial Science, The University of Tokyo, Pasadena, Tokyo, Japan
,
Cordelia Schmid
Institute of Industrial Science, INRIA, Montbonnot, Tokyo, France

Sponsors

TOYOTA: TOYOTA
Google Inc.
IBMR: IBM Research
NVIDIA
Microsoft Reasearch: Microsoft Reasearch

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 October 2012

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Cited By

Wang ZHuang JChen SZhang ZCai Z(2022)Target Tracking Algorithm Based on Multi-feature Adaptive Fusion in Complex ScenesProceedings of the 2022 2nd International Conference on Control and Intelligent Robotics10.1145/3548608.3559213(310-315)Online publication date: 24-Jun-2022
https://dl.acm.org/doi/10.1145/3548608.3559213
Liang YWang DChen YXiao LLiu C(2021)Robust Visual Tracking Based on Convolutional Sparse CodingWireless Communications & Mobile Computing10.1155/2021/55312222021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5531222
Lu XChen SZheng ZWeng CXu R(2021)Generalized Intersection over Union Based Online Weighted Multiple Instance Learning Algorithm for Object TrackingProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457698(111-116)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457698
Ge TLu Y(2021)Multiple kernel boosting based tracking using pooling features2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7351396(3210-3214)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/ICIP.2015.7351396
Testa MMagli E(2021)Compressive classification based on autoregressive features2016 International Conference on Communications (COMM)10.1109/ICComm.2016.7528324(433-438)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.1109/ICComm.2016.7528324
Wu YLin QJia HHassan MHu W(2020)Auto-KeyProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33810044:1(1-23)Online publication date: 18-Mar-2020
https://dl.acm.org/doi/10.1145/3381004
Su ZLi JChang JDu BXiao Y(2020)Real-time visual tracking using complementary kernel support correlation filtersFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-8116-114:2(417-429)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1007/s11704-018-8116-1
Wang YLuo XDing LFu SHu S(2020)Multi-task non-negative matrix factorization for visual object trackingPattern Analysis & Applications10.1007/s10044-019-00812-423:1(493-507)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10044-019-00812-4
Chen ZLiu PDu YLuo YGuo J(2020)Long-term correlation tracking via spatial–temporal contextThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01631-836:2(425-442)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s00371-019-01631-8
Guo GSun YJiang X(2020)A partitioned quasi-likelihood for distributed statistical inferenceComputational Statistics10.1007/s00180-020-00974-435:4(1577-1596)Online publication date: 1-Dec-2020
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