Hierarchical relaxed partitioning system for activity recognition

Faisal Ahzar, Chang-Tsun Li

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    A hierarchical relaxed partitioning system (HRPS) is proposed for recognizing similar activities which has a feature space with multiple overlaps. Two feature descriptors are built from the human motion analysis of a 2-D stick figure to represent cyclic and noncyclic activities. The HRPS first discerns the pure and impure activities, i.e., with no overlaps and multiple overlaps in the feature space, respectively, then tackles the multiple overlaps problem of the impure activities via an innovative majority voting scheme. The results show that the proposed method robustly recognizes various activities of two different resolution data sets, i.e., low and high (with different views). The advantage of HRPS lies in the real-time speed, ease of implementation and extension, and nonintensive training.
    Original languageEnglish
    Article number 7509603
    Pages (from-to)784 - 795
    Number of pages12
    JournalIEEE Transactions on Cybernetics
    Volume47
    Issue number3
    Early online date12 Jul 2016
    DOIs
    Publication statusPublished - 2017

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