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Action Recognition Using Local Visual Descriptors and Inertial Data

  • Taha AlhershEmail author
  • Samir Brahim Belhaouari
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

Different body sensors and modalities can be used in human action recognition, either separately or simultaneously. Multi-modal data can be used in recognizing human action. In this work we are using inertial measurement units (IMUs) positioned at left and right hands with first person vision for human action recognition. A novel statistical feature extraction method was proposed based on curvature of the graph of a function and tracking left and right hand positions in space. Local visual descriptors have been used as features for egocentric vision. An intermediate fusion between IMUs and visual sensors has been performed. Despite of using only two IMUs sensors with egocentric vision, our classification result achieved is 99.61% for recognizing nine different actions. Feature extraction step could play a vital step in human action recognition with limited number of sensors, hence, our method might indeed be promising.

Keywords

Human action recognition IMUs Visual descriptors Feature extraction Classification Sensor fusing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany
  2. 2.College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar

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