Online learning and fusion of orientation appearance models for robust rigid object tracking

Ioannis Marras, Joan Alabort, Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic

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    Abstract

    We present a robust framework for learning and fusing different modalities for rigid object tracking. Our method fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depths cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our method combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields provided by the Kinect. To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles. This kernel enables us to cope with gross measurement errors, missing data as well as typical problems in visual tracking such as illumination changes and occlusions. Additionally, the employed kernel can be efficiently implemented online. Finally, we propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original AAM. Thus the proposed learning and fusing framework is robust, exact, computationally efficient and does not require off-line training. By combining the proposed models with a particle filter, the proposed tracking framework achieved robust performance in very difficult tracking scenarios including extreme pose variations.
    Original languageUndefined
    Title of host publicationProceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages1-8
    Number of pages8
    ISBN (Print)978-1-4673-5545-2
    DOIs
    Publication statusPublished - Apr 2013
    Event10th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China
    Duration: 22 Apr 201326 Apr 2013
    Conference number: 10
    http://fg2013.cse.sc.edu/

    Publication series

    Name
    PublisherIEEE Computer Society

    Conference

    Conference10th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2013
    Abbreviated titleFG
    CountryChina
    CityShanghai
    Period22/04/1326/04/13
    Internet address

    Keywords

    • EWI-24277
    • HMI-HF: Human Factors
    • particle filtering (numerical methods)
    • Pose estimation
    • Target tracking
    • IR-89535
    • image fusion
    • EC Grant Agreement nr.: FP7/288235
    • EC Grant Agreement nr.: FP7/2007-2013
    • Gradient methods
    • METIS-302627
    • learning (artificial intelligence)

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