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Author (up) Antonio Hernandez; Sergio Escalera; Stan Sclaroff edit  doi
  Title Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures Type Journal Article
  Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 118 Issue 1 Pages 49–64  
  Keywords Contextual rescoring; Poselets; Human pose estimation  
  Abstract In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly.  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ HES2016 Serial 2719  
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