A flexible sequence alignment approach on pattern mining and matching for human activity recognition

Po Cheng Huang, Sz Shian Lee, Yaw Huang Kuo, Kuan Rong Lee

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

This paper proposes a flexible sequence alignment approach for pattern mining and matching in the recognition of human activities. During pattern mining, the proposed sequence alignment algorithm is invoked to extract out the representative patterns which denote specific activities of a person from the training patterns. It features high performance and robustness on pattern diversity. Besides, the algorithm evaluates the appearance probability of each pattern as weight and allows adapting pattern length to various human activities. Both of them are able to improve the accuracy of activity recognition. In pattern matching, the proposed algorithm adopts a dynamic programming based strategy to evaluate the correlation degree between each representative activity pattern and the observed activity sequence. It can avoid the trouble on segmenting the observed sequence. Moreover, we are able to obtain recognition results continuously. Besides, the proposed matching algorithm favors recognition of concurrent human activities with parallel matching. The experimental result confirms the high accuracy of human activity recognition by the proposed approach.

Original languageEnglish
Pages (from-to)298-306
Number of pages9
JournalExpert Systems With Applications
Volume37
Issue number1
DOIs
Publication statusPublished - 2010 Jan 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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