Video-based human movement analysis and its application to surveillance systems

Jun-Wei Hsieh*, Yung Tai Hsu, Hong Yuan Mark Liao, Chih Chiang Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

110 Scopus citations


This paper presents a novel posture classification system that analyzes human movements directly from video sequences. In the system, each sequence of movements is converted into a posture sequence. To better characterize a posture in a sequence, we triangulate it into triangular meshes, from which we extract two features: the skeleton feature and the centroid context feature. The first feature is used as a coarse representation of the subject, while the second is used to derive a finer description. We adopt a depth-first search (dfs) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is more robust and efficient than conventional silhouette-based approaches. The skeletal features extracted in the first stage are used to extract the centroid context feature, which is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process because they generate a set of key postures from a movement sequence. The ordered key posture sequence is represented by a symbol string. Matching two arbitrary action sequences then becomes a symbol string matching problem. Our experiment results demonstrate that the proposed method is a robust, accurate, and powerful tool for human movement analysis.

Original languageEnglish
Article number4456691
Pages (from-to)372-384
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number3
StatePublished - 1 Apr 2008


  • Behavior analysis
  • Event detection
  • String matching
  • Triangulation


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