Histogram-Based Description of Local Space-Time Appearance View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Karla Brkić , Axel Pinz , Siniša Šegvić , Zoran Kalafatić

ABSTRACT

We introduce a novel local spatio-temporal descriptor intended to model the spatio-temporal behavior of a tracked object of interest in a general manner. The basic idea of the descriptor is the accumulation of histograms of an image function value through time. The histograms are calculated over a regular grid of patches inside the bounding box of the object and normalized to represent empirical probability distributions. The number of grid patches is fixed, so the descriptor is invariant to changes in spatial scale. Depending on the temporal complexity/details at hand, we introduce “first order STA descriptors” that describe the average distribution of a chosen image function over time, and “second order STA descriptors” that model the distribution of each histogram bin over time. We discuss entropy and χ2 as well-suited similarity and saliency measures for our descriptors. Our experimental validation ranges from the patch- to the object-level. Our results show that STA, this simple, yet powerful novel description of local space-time appearance is well-suited to machine learning and will be useful in video-analysis, including potential applications of object detection, tracking, and background modeling. More... »

PAGES

206-217

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2001-11. Saliency, Scale and Image Description in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2006. Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support in COMPUTER VISION – ECCV 2006
  • 2005-07. Detecting Pedestrians Using Patterns of Motion and Appearance in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010-04. Human action detection via boosted local motion histograms in MACHINE VISION AND APPLICATIONS
  • 2011. Mobile Surveillance by 3D-Outlier Analysis in COMPUTER VISION – ACCV 2010 WORKSHOPS
  • 1994. Scale-Space Theory in Computer Vision in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-21227-7_20

    DOI

    http://dx.doi.org/10.1007/978-3-642-21227-7_20

    DIMENSIONS

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