Human motion recognition using support vector machines

Dongwei Cao, Osama T. Masoud, Daniel Boley, Nikolaos Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.

Original languageEnglish (US)
Pages (from-to)1064-1075
Number of pages12
JournalComputer Vision and Image Understanding
Volume113
Issue number10
DOIs
StatePublished - Oct 1 2009

Keywords

  • Human motion recognition
  • Recursive filtering
  • Support vector machine

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