Local derivative pattern for action recognition in depth images View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-04

AUTHORS

Xuan Son Nguyen, Thanh Phuong Nguyen, François Charpillet, Ngoc-Son Vu

ABSTRACT

This paper proposes a new local descriptor for action recognition in depth images using second-order directional Local Derivative Patterns (LDPs). LDP relies on local derivative direction variations to capture local patterns contained in an image region. Our proposed local descriptor combines different directional LDPs computed from three depth maps obtained by representing depth sequences in three orthogonal views and is able to jointly encode the shape and motion cues. Moreover, we suggest the use of Sparse Coding-based Fisher Vector (SCFVC) for encoding local descriptors into a global representation of depth sequences. SCFVC has been proven effective for object recognition but has not gained much attention for action recognition. We perform action recognition using Extreme Learning Machine (ELM). Experimental results on three public benchmark datasets show the effectiveness of the proposed approach. More... »

PAGES

8531-8549

References to SciGraph publications

  • 2007. Information Retrieval for Music and Motion in NONE
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2016-06. Real-time human action recognition based on depth motion maps in JOURNAL OF REAL-TIME IMAGE PROCESSING
  • 2013-12. Image Classification with the Fisher Vector: Theory and Practice in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Robust 3D Action Recognition with Random Occupancy Patterns in COMPUTER VISION – ECCV 2012
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s11042-017-4749-z

    DOI

    http://dx.doi.org/10.1007/s11042-017-4749-z

    DIMENSIONS

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