• 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 30, 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.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
A. Farooq, F. Farooq, A. V. 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]
Adnan Farooq, Faisal Farooq, and Anh Vu Le. 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 .

[BibTeX Style]
@article{tiis:21770, title="Human Action Recognition via Depth Maps Body Parts of Action", author="Adnan Farooq and Faisal Farooq and Anh Vu Le and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2018.05.023 }, volume={12}, number={5}, year="2018", month={May}, pages={2327-2347}}