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Gesture recognition using depth images

Published: 09 December 2013 Publication History

Abstract

This work presents an approach for recognizing 3D human gestures by using depth images. The proposed motion trail model (MTM) consists of both motion information and static posture information over the gesture sequence along the xoy-plane. By projecting depth images onto other two planes in 3D space, gestures can be represented with complementary information from additional planes. Accordingly 2D-MTM can be extended into 3D space in addition to the lateral scene parallel to the image plane to generate 3D-MTM. The Histogram of Oriented Gradient (HOG) is then extracted from the proposed 3D-MTM as the feature descriptor. The final recognition of gestures is performed through maximum correlation coefficient. The preliminary results demonstrate the average error rate decreases from 62.80% of baseline method to 21.74% after using the proposed approach on Chalearn gesture dataset.

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cover image ACM Conferences
ICMI '13: Proceedings of the 15th ACM on International conference on multimodal interaction
December 2013
630 pages
ISBN:9781450321297
DOI:10.1145/2522848
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 09 December 2013

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Author Tags

  1. 3d motino trail model
  2. depth images
  3. gesture recognition
  4. kinect

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ICMI '13 Paper Acceptance Rate 49 of 133 submissions, 37%;
Overall Acceptance Rate 453 of 1,080 submissions, 42%

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  • (2024)Zero-Shot Underwater Gesture RecognitionPattern Recognition10.1007/978-3-031-78183-4_22(346-361)Online publication date: 4-Dec-2024
  • (2017)Review of constraints on vision‐based gesture recognition for human–computer interactionIET Computer Vision10.1049/iet-cvi.2017.005212:1(3-15)Online publication date: 18-Dec-2017
  • (2016)Survey on 3D Hand Gesture RecognitionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.246955126:9(1659-1673)Online publication date: 1-Sep-2016
  • (2014)Real-time hand gesture recognition with Kinect for playing racing video games2014 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2014.6889481(3240-3246)Online publication date: Jul-2014

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