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Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation

  • Vivek Kumar Singh
  • Ram Nevatia
  • Chang Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

We address the problem of estimating human pose in a single image using a part based approach. Pose accuracy is directly affected by the accuracy of the part detectors but more accurate detectors are likely to be also more computationally expensive. We propose to use multiple, heterogeneous part detectors with varying accuracy and computation requirements, ordered in a hierarchy, to achieve more accurate and efficient pose estimation. For inference, we propose an algorithm to localize articulated objects by exploiting an ordered hierarchy of detectors with increasing accuracy. The inference uses branch and bound method to search for each part and use kinematics from neighboring parts to guide the branching behavior and compute bounds on the best part estimate. We demonstrate our approach on a publicly available People dataset and outperform the state-of-art methods. Our inference is 3 times faster than one based on using a single, highly accurate detector.

Keywords

Part Detector Pictorial Structure Region Template Active Branch Perceptron Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vivek Kumar Singh
    • 1
  • Ram Nevatia
    • 1
  • Chang Huang
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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