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Robust Recognition Algorithm for Fall Down Behavior

  • Wei Yan
  • Jianbin Xie
  • Peiqin Li
  • Tong Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Detecting fall down behavior is a meaningful work in the area of public video surveillance and smart home care, as this behavior is often caused by accident but usually trigger serious result. However, the uncertain individual behavior, the difference between different cameras, and the complexity of real application scene make the work absolutely hard. In this paper, a robust fall down behavior recognition algorithm is proposed based on the spatial and temporal analysis of the Key Area of Human Body (KAHB). Firstly, a modified ViBe method is applied to extract motion area. Then a pre-trained human body classifier combined with histogram tracking is used to locate the KAHB and extract its normalized spatial and temporal features. Finally, a SVM classifier is employed to find the fall down behavior.

Keywords

Fall down behavior recognition Visual computation Smart surveillance 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina

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