Spatio-Temporal Phrases for Activity Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Yimeng Zhang , Xiaoming Liu , Ming-Ching Chang , Weina Ge , Tsuhan Chen

ABSTRACT

The local feature based approaches have become popular for activity recognition. A local feature captures the local movement and appearance of a local region in a video, and thus can be ambiguous; e.g., it cannot tell whether a movement is from a person’s hand or foot, when the camera is far away from the person. To better distinguish different types of activities, people have proposed using the combination of local features to encode the relationships of local movements. Due to the computation limit, previous work only creates a combination from neighboring features in space and/or time. In this paper, we propose an approach that efficiently identifies both local and long-range motion interactions; taking the “push” activity as an example, our approach can capture the combination of the hand movement of one person and the foot response of another person, the local features of which are both spatially and temporally far away from each other. Our computational complexity is in linear time to the number of local features in a video. The extensive experiments show that our approach is generically effective for recognizing a wide variety of activities and activities spanning a long term, compared to a number of state-of-the-art methods. More... »

PAGES

707-721

References to SciGraph publications

  • 2008. An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector in COMPUTER VISION – ECCV 2008
  • 2008. Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners in COMPUTER VISION – ECCV 2008
  • 2010. Variations of a Hough-Voting Action Recognition System in RECOGNIZING PATTERNS IN SIGNALS, SPEECH, IMAGES AND VIDEOS
  • 2010. An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010 in RECOGNIZING PATTERNS IN SIGNALS, SPEECH, IMAGES AND VIDEOS
  • Book

    TITLE

    Computer Vision – ECCV 2012

    ISBN

    978-3-642-33711-6
    978-3-642-33712-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33712-3_51

    DOI

    http://dx.doi.org/10.1007/978-3-642-33712-3_51

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011142796


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