• DocumentCode
    3001086
  • Title

    Pose search: Retrieving people using their pose

  • Author

    Ferrari, V. ; Marin-Jimenez, Manuel ; Zisserman, Andrew

  • Author_Institution
    ETH Zurich, Zurich, Switzerland
  • fYear
    2009
  • fDate
    20-25 June 2009
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    We describe a method for retrieving shots containing a particular 2D human pose from unconstrained movie and TV videos. The method involves first localizing the spatial layout of the head, torso and limbs in individual frames using pictorial structures, and associating these through a shot by tracking. A feature vector describing the pose is then constructed from the pictorial structure. Shots can be retrieved either by querying on a single frame with the desired pose, or through a pose classifier trained from a set of pose examples. Our main contribution is an effective system for retrieving people based on their pose, and in particular we propose and investigate several pose descriptors which are person, clothing, background and lighting independent. As a second contribution, we improve the performance over existing methods for localizing upper body layout on unconstrained video. We compare the spatial layout pose retrieval to a baseline method where poses are retrieved using a HOG descriptor. Performance is assessed on five episodes of the TV series ´Buffy the Vampire Slayer´, and pose retrieval is demonstrated also on three Hollywood movies..
  • Keywords
    image retrieval; pose estimation; video signal processing; 2D human pose; HOG descriptor; pictorial structures; pose descriptors; pose search; retrieving people; retrieving shots; spatial layout pose retrieval; unconstrained video; Clothing; Humans; Image recognition; Information retrieval; Motion pictures; Spatial databases; TV; Torso; Videos; Yarn;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
  • Conference_Location
    Miami, FL
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4244-3992-8
  • Type

    conf

  • DOI
    10.1109/CVPR.2009.5206495
  • Filename
    5206495