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  • Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors

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    Accepted Manuscript (AM)
    Author(s)
    Carvajal, Johanna
    McCool, Chris
    Lovell, Brian
    Sanderson, Conrad
    Griffith University Author(s)
    Sanderson, Conrad
    Year published
    2016
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    Abstract
    We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation. A given video (containing several consecutive actions) is processed via a sequence of overlapping temporal windows. Each frame in a temporal window is represented through selective low-level spatio-temporal features which efficiently capture relevant local dynamics. Features from each window are represented as a Fisher vector, which captures first and second order statistics. Instead of directly classifying each Fisher vector, it is converted into a vector of class probabilities. The final classification decision ...
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    We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation. A given video (containing several consecutive actions) is processed via a sequence of overlapping temporal windows. Each frame in a temporal window is represented through selective low-level spatio-temporal features which efficiently capture relevant local dynamics. Features from each window are represented as a Fisher vector, which captures first and second order statistics. Instead of directly classifying each Fisher vector, it is converted into a vector of class probabilities. The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows. Experiments were performed on two datasets: s-KTH (a stitched version of the KTH dataset to simulate multi-actions), and the challenging CMU-MMAC dataset. On s-KTH, the proposed approach achieves an accuracy of 85.0 %, significantly outperforming two recent approaches based on GMMs and HMMs which obtained 78.3 % and 71.2 %, respectively. On CMU-MMAC, the proposed approach achieves an accuracy of 40.9 %, outperforming the GMM and HMM approaches which obtained 33.7 % and 38.4 %, respectively. Furthermore, the proposed system is on average 40 times faster than the GMM based approach.
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    Conference Title
    Lecture Notes in Computer Science
    Volume
    9794
    DOI
    https://doi.org/10.1007/978-3-319-42996-0_10
    Copyright Statement
    © Springer International Publishing Switzerland 2016. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/395913
    Collection
    • Conference outputs

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