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HOG-Based Descriptors on Rotation Invariant Human Detection

  • Panachit Kittipanya-ngam
  • Eng How Lung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

In the past decade, there have been many proposed techniques on human detection. Dalal and Triggs suggested Histogram of Oriented Gradient (HOG) features combined with a linear SVM to handle the task. Since then, there have been many variations of HOG-based detection introduced. They are, nevertheless, based on an assumption that the human must be in upright pose due to the limitation in geometrical variation. HOG-based human detections obviously fails in monitoring human activities in the daily life such as sleeping, lying down, falling, and squatting. This paper focuses on exploring various features based on HOG for rotation invariant human detection. The results show that square-shaped window can cover more poses but will cause a drop in performance. Moreover, some rotation-invariant techniques used in image retrieval outperform other techniques in human classification on upright pose and perform very well on various poses. This could help in neglecting the assumption of upright pose generally used.

Keywords

Image Retrieval Detection Window Human Detection Oriented Gradient Pedestrian Detection 
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 2011

Authors and Affiliations

  • Panachit Kittipanya-ngam
    • 1
  • Eng How Lung
    • 1
  1. 1.Institute for Infocomme ResearchSingapore

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