Exploring Sparseness and Self-Similarity for Action Recognition

    Authors

    C. Sun; I. N. Junejo; M. Tappen;H. Foroosh

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Image Process.

    Keywords

    Action recognition; self-similarity; tensor approximation; REPRESENTATION; FEATURES; CONTEXT; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    We propose that the dynamics of an action in video data forms a sparse self-similar manifold in the space-time volume, which can be fully characterized by a linear rank decomposition. Inspired by the recurrence plot theory, we introduce the concept of Joint Self-Similarity Volume (Joint-SSV) to model this sparse action manifold, and hence propose a new optimized rank-1 tensor approximation of the Joint-SSV to obtain compact low-dimensional descriptors that very accurately characterize an action in a video sequence. We show that these descriptor vectors make it possible to recognize actions without explicitly aligning the videos in time in order to compensate for speed of execution or differences in video frame rates. Moreover, we show that the proposed method is generic, in the sense that it can be applied using different low-level features, such as silhouettes, tracked points, histogram of oriented gradients, and so forth. Therefore, our method does not necessarily require explicit tracking of features in the space-time volume. Our experimental results on five public data sets demonstrate that our method produces promising results and outperforms many baseline methods.

    Journal Title

    Ieee Transactions on Image Processing

    Volume

    24

    Issue/Number

    8

    Publication Date

    1-1-2015

    Document Type

    Article

    Language

    English

    First Page

    2488

    Last Page

    2501

    WOS Identifier

    WOS:000354441200002

    ISSN

    1057-7149

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