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Signal, Image and Video Processing

, Volume 10, Issue 3, pp 585–592 | Cite as

Sparse representation-based human detection: a scale-embedded dictionary approach

  • G. Krishna Vinay
  • S. M. Haque
  • R. Venkatesh Babu
  • K. R. Ramakrishnan
Original Paper

Abstract

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.

Keywords

Human detection Histogram of oriented gradients (HOG) \(l_{1}\)-Norm minimization Sparse representation Sparse classification Scale-embedded dictionary 

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • G. Krishna Vinay
    • 1
  • S. M. Haque
    • 1
  • R. Venkatesh Babu
    • 2
  • K. R. Ramakrishnan
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.SERCIndian Institute of ScienceBangaloreIndia

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