• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Human Action Recognition via Depth Maps Body Parts of Action

Vol. 12, No.5, May 31, 2018
10.3837/tiis.2018.05.023 , Download Paper (Free):

Abstract

Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.


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Cite this article

[IEEE Style]
Adnan Farooq, Faisal Farooq and Anh Vu Le, "Human Action Recognition via Depth Maps Body Parts of Action," KSII Transactions on Internet and Information Systems, vol. 12, no. 5, pp. 2327-2347, 2018. DOI: 10.3837/tiis.2018.05.023

[ACM Style]
Farooq, A., Farooq, F., and Le, A. V. 2018. Human Action Recognition via Depth Maps Body Parts of Action. KSII Transactions on Internet and Information Systems, 12, 5, (2018), 2327-2347. DOI: 10.3837/tiis.2018.05.023