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Trajectory Based Integrated Features for Action Classification from Depth Data

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 841)

Abstract

We present an approach for Human Action Recognition based on amalgamation of features from depth maps and body-joint data. This Integrated feature set consists of depth features based on gradient orientation and motion energy, in addition to features from 3D- skeleton data capturing its statistical details. Feature selection is carried out to extract a relevant set of features for action recognition. The resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset.

Keywords

Skeleton Data Depth Map Activity Recognition Human Action Recognition ReliefF 
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 Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Indian Institute of TechnologyDelhiIndia
  2. 2.Works ApplicationSingaporeSingapore
  3. 3.Bennett UniversityGreater NoidaIndia

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