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Fast Multi-aspect 2D Human Detection

  • Tai-Peng Tian
  • Stan Sclaroff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

We address the problem of detecting human figures in images, taking into account that the image of the human figure may be taken from a range of viewpoints. We capture the geometric deformations of the 2D human figure using an extension of the Common Factor Model (CFM) of Lan and Huttenlocher. The key contribution of the paper is an improved iterative message passing inference algorithm that runs faster than the original CFM algorithm. This is based on the insight that messages created using the distance transform are shift invariant and therefore messages can be created once and then shifted for subsequent iterations. Since shifting (O(1) complexity) is faster than computing a distance transform (O(n) complexity), a significant speedup is observed in the experiments. We demonstrate the effectiveness of the new model for the human parsing problem using the Iterative Parsing data set and results are competitive with the state of the art detection algorithm of Andriluka, et al.

Keywords

Body Part Detection Result Inference Algorithm Compatibility Function Viewpoint Change 
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

  • Tai-Peng Tian
    • 1
  • Stan Sclaroff
    • 1
  1. 1.Department of Computer ScienceBoston University 

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