• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Histogram of oriented rectangles: a new pose descriptor for human action recognition

      Thumbnail
      View / Download
      1011.8 Kb
      Author
      İkizler, N.
      Duygulu, P.
      Date
      2009-09-02
      Source Title
      Image and Vision Computing
      Print ISSN
      0262-8856
      Publisher
      Elsevier BV
      Volume
      27
      Issue
      10
      Pages
      1515 - 1526
      Language
      English
      Type
      Article
      Item Usage Stats
      127
      views
      165
      downloads
      Abstract
      Most of the approaches to human action recognition tend to form complex models which require lots of parameter estimation and computation time. In this study, we show that, human actions can be simply represented by pose without dealing with the complex representation of dynamics. Based on this idea, we propose a novel pose descriptor which we name as Histogram-of-Oriented-Rectangles (HOR) for representing and recognizing human actions in videos. We represent each human pose in an action sequence by oriented rectangular patches extracted over the human silhouette. We then form spatial oriented histograms to represent the distribution of these rectangular patches. We make use of several matching strategies to carry the information from the spatial domain described by the HOR descriptor to temporal domain. These are (i) nearest neighbor classification, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis to rectangular patches, (iii) a classifier-based approach using Support Vector Machines, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the HOR descriptor. For the cases when pose descriptor is not sufficiently strong alone, such as to differentiate actions "jogging" and "running", we also incorporate a simple velocity descriptor as a prior to the pose based classification step. We test our system with different configurations and experiment on two commonly used action datasets: the Weizmann dataset and the KTH dataset. Results show that our method is superior to other methods on Weizmann dataset with a perfect accuracy rate of 100%, and is comparable to the other methods on KTH dataset with a very high success rate close to 90%. These results prove that with a simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © 2009 Elsevier B.V. All rights reserved.
      Keywords
      Action recognition
      Human motion understanding
      Pose descriptor
      Accuracy rate
      Action sequences
      Compact representation
      Complex model
      Computation time
      Data sets
      Descriptor
      Dynamic time warping
      Histogramming
      Human pose
      Human silhouette
      Human-action recognition
      Motion energy
      Nearest neighbor classification
      Rectangular patch
      Robust recognition
      Spatial domains
      Temporal domain
      Temporal representations
      Gesture recognition
      Parameter estimation
      Statistical tests
      Human form models
      Permalink
      http://hdl.handle.net/11693/22633
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.imavis.2009.02.002
      Collections
      • Department of Computer Engineering 1413
      Show full item record

      Related items

      Showing items related by title, author, creator and subject.

      • Thumbnail

        Two-person interaction recognition via spatial multiple instance embedding 

        Sener F.; Ikizler-Cinbis, N. (Academic Press Inc., 2015)
        Abstract In this work, we look into the problem of recognizing two-person interactions in videos. Our method integrates multiple visual features in a weakly supervised manner by utilizing an embedding-based multiple instance ...
      • Thumbnail

        Karşılıklı bilgi ölçütü kullanılarak giyilebilir hareket duyucu sinyallerinin aktivite tanıma amaçlı analizi 

        Dobrucalı, Oğuzcan; Barshan, Billur (IEEE, 2014-04)
        Giyilebilir hareket duyucuları ile insan aktivitelerinin saptanmasında, uygun duyucu yapılanışının seçimi önem taşıyan bir konudur. Bu konu, kullanılacak duyucuların sayısının, türünün, sabitlenecekleri konum ve yönelimin ...
      • Thumbnail

        Recognizing human actions from noisy videos via multiple instance learning 

        şener, Fadime; Samet, Nermin; Duygulu, Pınar; Ikizler-Cinbis, N. (IEEE, 2013)
        In this work, we study the task of recognizing human actions from noisy videos and effects of noise to recognition performance and propose a possible solution. Datasets available in computer vision literature are relatively ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy