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Improved GLOH approach for one-shot learning human gesture recognition

  • Nabin Kumar Karn*
  • , Feng Jiang
  • *Corresponding author for this work
  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A method is presented for One-Shot Learning Human Gesture Recognition. Shi-Tomasi corner detector and sparse optical flow are used to quickly detect and track robust key-points around motion patterns in scale space. Then Improved Gradient Location and Orientation Histogram feature descriptor is applied to capture the description of robust key interest point. All the extracted features from the training samples are clustered with the k-means algorithm to learn a visual codebook. Subsequently, simulation orthogonal matching pursuit is applied to achieve descriptor coding which map each feature into a certain visual codeword. K-NN classifier is used to recognizing the gesture. The proposed approach has been evaluated on ChaLearn gesture database.

Original languageEnglish
Title of host publicationBiometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings
EditorsShiguang Shan, Zhisheng You, Jie Zhou, Weishi Zheng, Yunhong Wang, Zhenan Sun, Jianjiang Feng, Qijun Zhao
PublisherSpringer Verlag
Pages441-452
Number of pages12
ISBN (Print)9783319466538
DOIs
StatePublished - 2016
Externally publishedYes
Event11th Chinese Conference on Biometric Recognition, CCBR 2016 - Chengdu, China
Duration: 14 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9967 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Chinese Conference on Biometric Recognition, CCBR 2016
Country/TerritoryChina
CityChengdu
Period14/10/1616/10/16

Keywords

  • Bag of features (BoF) model
  • Feature extraction
  • Gesture
  • Improved gradient location and orientation histogram (IGLOH)
  • K-means algorithms

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