Motion Intensity Code for Action Recognition in Video Using PCA and SVM

  • J. Arunnehru
  • M. Kalaiselvi Geetha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


Manual video surveillance is highly expensive and inconvenient in continuous monitoring, by a security personnel. So automatic video surveillance and activity recognition is needed. In this paper, an activity recognition approach is proposed, the difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on KTH dataset, considering four activities viz (walking, running, waving and boxing) and Weizmann dataset, considering four activities viz (walking, running, waving one hand, waving both hands) with Support Vector Machines (SVM) for classification. This approach shows an overall performance of 94.75% using KTH dataset and 92% using Weizmann dataset to recognize the actions. The performance of the proposed approach is comparable with well known existing methods.


Video Surveillance Activity Recognition Gesture Recognition Principal Component Analysis Support Vector Machines Motion Intensity Code 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • J. Arunnehru
    • 1
  • M. Kalaiselvi Geetha
    • 1
  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityIndia

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