• DocumentCode
    254356
  • Title

    Using k-Poselets for Detecting People and Localizing Their Keypoints

  • Author

    Gkioxari, Georgia ; Hariharan, Balaji ; Girshick, Ross ; Malik, Jagannath

  • Author_Institution
    Univ. of California, Berkeley, Berkeley, CA, USA
  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    3582
  • Lastpage
    3589
  • Abstract
    A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
  • Keywords
    feature extraction; object detection; object recognition; prediction theory; CNN features; DPM; competitive detection performance; deformable part model; deformation cost; ground-truth annotations; k-poselet; keypoint prediction; keypoints configuration; keypoints localization; object recognition; people detection; person detection; Deformable models; Detectors; Face; Feature extraction; Torso; Training; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPR.2014.458
  • Filename
    6909853