Shift Invariant Dictionary Learning for Human Action Recognition
Ushapreethi P1, Lakshmi Priya G G2

1Ushapreethi P, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore.
2Lakshmi Priya G G, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore. 

Manuscript received on November 13, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 2107-2111 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7005129219/2019©BEIESP | DOI: 10.35940/ijitee.B7005.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Sparse representation is an emerging topic among researchers. The method to represent the huge volume of dense data as sparse data is much needed for various fields such as classification, compression and signal denoising. The base of the sparse representation is dictionary learning. In most of the dictionary learning approaches, the dictionary is learnt based on the input training signals which consumes more time. To solve this issue, the shift-invariant dictionary is used for action recognition in this work. Shift-Invariant Dictionary (SID) is that the dictionary is constructed in the initial stage with shift-invariance of initial atoms. The advantage of the proposed SID based action recognition method is that it requires minimum training time and achieves highest accuracy. 
Keywords: Sparse Representation, Action Recognition, Sparse Coding, Shift Invariant Dictionaries, SVM Classifier
Scope of the Article: Pattern Recognition