Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 203-209, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-203-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 203-209, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-203-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

ACTION RECOGNITION USING UNDECIMATED DUAL TREE COMPLEX WAVELET TRANSFORM FROM DEPTH MOTION MAPS / DEPTH SEQUENCES

B. H. Shekar1, P. Rathnakara Shetty1, M. Sharmila Kumari2, and L. Mestetsky3 B. H. Shekar et al.
  • 1Mangalore University, Mangalore, Karnataka, India
  • 2P A College of Engineering, Mangalore, Karnataka, India
  • 3Lomonosov Moscow State University, Moscow, Russia

Keywords: Depth Maps, Wavelet Transform, Stridden Depth Motion Map, Action Recognition

Abstract. Accumulating the motion information from a video sequence is one of the highly challenging and significant phase in Human Action Recognition. To achieve this, several classical and compact representations are proposed by the research community with proven applicability. In this paper, we propose a compact Depth Motion Map based representation methodology with hastey striding, consisely accumulating the motion information. We extract Undecimated Dual Tree Complex Wavelet Transform features from the proposed DMM, to form an efficient feature descriptor. We designate a Sequential Extreme Learning Machine for classifying the human action secquences on benchmark datasets, MSR Action 3D dataset and DHA Dataset. We empirically prove the feasability of our method under standard protocols, achieving proven results.