Real-Time Classification of Lying Bodies by HOG Descriptors

  • A. Beltrán-Herrera
  • E. Vázquez-Santacruz
  • M. Gamboa-Zuñiga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)


In this paper we show a methodology for bodies classification in lying state using HOG descriptor and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Due to current technology a limited number of sensors is used, wich results in low resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.


Support Vector Machine Pressure Sensor Support Vector Machine Model Gradient Direction Image Descriptor 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • A. Beltrán-Herrera
    • 1
  • E. Vázquez-Santacruz
    • 1
  • M. Gamboa-Zuñiga
    • 1
  1. 1.CGSTICCenter for Research and Advanced Studies of the National of Polytechnic Institute of Mexico (Cinvestav-IPN)México D.F.México

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