Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion

Behzad BOZORGTABAR, Roland GOECKE

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as our main model as well as an image patch tracker as our auxiliary model. Firstly, we randomly sample image patches within the target region obtained by the main model in the training frames for constructing a visual vocabulary using Histogram of Oriented Gradient features. Secondly, we use a supervised learning algorithm based on a Gaussian Mixture Model, which not only operates on supervised information to improve the discriminative power of the clusters, but also increases the purity of the clusters. Then, auxiliary models are initialised by obtaining confidence scores of image patches based on the similarity between candidates and codewords. In addition, an updating procedure and a result refinement scheme are included in the proposed tracking approach. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose variation and rotation
    Original languageEnglish
    Title of host publication 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013)
    EditorsPaulo de Souza, Ulrich Engelke, Ashfaqur Rahman
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages442-449
    Number of pages8
    ISBN (Print)9781479921263
    DOIs
    Publication statusPublished - 2013
    Event2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Hobart, Hobart, Australia
    Duration: 26 Nov 201328 Nov 2013
    http://staff.itee.uq.edu.au/lovell/aprs/dicta13/ (Conference Webpage)

    Conference

    Conference2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Abbreviated titleDICTA 2013
    CountryAustralia
    CityHobart
    Period26/11/1328/11/13
    Internet address

    Fingerprint

    Fusion reactions
    Supervised learning
    Electric fuses
    Principal component analysis
    Learning algorithms
    Experiments

    Cite this

    BOZORGTABAR, B., & GOECKE, R. (2013). Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion. In P. D. Souza, U. Engelke, & A. Rahman (Eds.), 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013) (pp. 442-449). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2013.6691525
    BOZORGTABAR, Behzad ; GOECKE, Roland. / Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion. 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). editor / Paulo de Souza ; Ulrich Engelke ; Ashfaqur Rahman. United States : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 442-449
    @inproceedings{47139e358b8542f5bcaa7c21b3ef0c9b,
    title = "Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion",
    abstract = "In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as our main model as well as an image patch tracker as our auxiliary model. Firstly, we randomly sample image patches within the target region obtained by the main model in the training frames for constructing a visual vocabulary using Histogram of Oriented Gradient features. Secondly, we use a supervised learning algorithm based on a Gaussian Mixture Model, which not only operates on supervised information to improve the discriminative power of the clusters, but also increases the purity of the clusters. Then, auxiliary models are initialised by obtaining confidence scores of image patches based on the similarity between candidates and codewords. In addition, an updating procedure and a result refinement scheme are included in the proposed tracking approach. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose variation and rotation",
    keywords = "Visual Tracking, Incremental Multiple PCA, Hierarchical Model Fusion",
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    booktitle = "2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013)",
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    BOZORGTABAR, B & GOECKE, R 2013, Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion. in PD Souza, U Engelke & A Rahman (eds), 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 442-449, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, Australia, 26/11/13. https://doi.org/10.1109/DICTA.2013.6691525

    Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion. / BOZORGTABAR, Behzad; GOECKE, Roland.

    2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). ed. / Paulo de Souza; Ulrich Engelke; Ashfaqur Rahman. United States : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 442-449.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    N2 - In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as our main model as well as an image patch tracker as our auxiliary model. Firstly, we randomly sample image patches within the target region obtained by the main model in the training frames for constructing a visual vocabulary using Histogram of Oriented Gradient features. Secondly, we use a supervised learning algorithm based on a Gaussian Mixture Model, which not only operates on supervised information to improve the discriminative power of the clusters, but also increases the purity of the clusters. Then, auxiliary models are initialised by obtaining confidence scores of image patches based on the similarity between candidates and codewords. In addition, an updating procedure and a result refinement scheme are included in the proposed tracking approach. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose variation and rotation

    AB - In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as our main model as well as an image patch tracker as our auxiliary model. Firstly, we randomly sample image patches within the target region obtained by the main model in the training frames for constructing a visual vocabulary using Histogram of Oriented Gradient features. Secondly, we use a supervised learning algorithm based on a Gaussian Mixture Model, which not only operates on supervised information to improve the discriminative power of the clusters, but also increases the purity of the clusters. Then, auxiliary models are initialised by obtaining confidence scores of image patches based on the similarity between candidates and codewords. In addition, an updating procedure and a result refinement scheme are included in the proposed tracking approach. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose variation and rotation

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    BOZORGTABAR B, GOECKE R. Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion. In Souza PD, Engelke U, Rahman A, editors, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). United States: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 442-449 https://doi.org/10.1109/DICTA.2013.6691525