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Cooking gesture recognition using local feature and depth image

Authors:

Rin-ichiro TaniguchiAuthors Info & Claims

CEA '12: Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities

Pages 37 - 42

https://doi.org/10.1145/2390776.2390785

Published: 02 November 2012 Publication History

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Abstract

In this paper, we propose a method combining visual local features and depth image information to recognize cooking gestures. We employ the feature calculation method[2] which used extended FAST detector and a compact descriptor CHOG3D to calculate visual local features. We pack the local features by BoW in frame sequences to represent the cooking gestures. In addition, the depth images of hands gestures are extracted and integrated spatio-temporally to represent the position and trajectory information of cooking gestures. The two kinds of features are used to describe cooking gestures, and recognition is realized by employing the SVM. In our method, we determine the gesture class for each frame in cooking sequences. By analyzing the results of frames, we recognize cooking gestures in a continue frame sequences of cooking menus, and find the temporal positions of the recognized gestures.

References

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[2]

Y. Ji, A. Shimada, and R. Taniguchi. A compact 3d descriptor in roi for human action recognition. In IEEE TENCON, 2010.

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B. Packer, K. Saenko, and D. Koller. A combined pose, object, and feature model for action understanding. In CVPR, 2012.

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M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele. A database for fine grained activity detection of cooking activities. In CVPR, 2012.

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L. Rybok, S. Friedberger, U. D. Hanebeck, and R. Stiefelhagen. The kit robo-kitchen data set for the evaluation of view-based activity recognition systems. In IEEE-RAS International Conference on Humanoid Robots, 2011.

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Cited By

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Ji YYang YShen FShen HZheng W(2021)Arbitrary-View Human Action Recognition: A Varying-View RGB-D Action DatasetIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297584531:1(289-300)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.2975845
Ji YXu FYang YShen FShen HZheng WBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)A Large-scale RGB-D Database for Arbitrary-view Human Action RecognitionProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240675(1510-1518)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240675
Korpela JMaekawa T(2018)Privacy preserving recognition of object-based activities using near-infrared reflective markersPersonal and Ubiquitous Computing10.1007/s00779-017-1070-922:2(365-377)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s00779-017-1070-9
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Index Terms

Cooking gesture recognition using local feature and depth image
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

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Published In

CEA '12: Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities

November 2012

72 pages

ISBN:9781450315920

DOI:10.1145/2390776

General Chair:
Mutsuo Sano
Osaka Institute of Technology, Japan
,
Program Chair:
Ichiro Ide
Nagoya University, Japan

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 02 November 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

November 2, 2012

Nara, Japan

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Overall Acceptance Rate 20 of 33 submissions, 61%

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Ji YYang YShen FShen HZheng W(2021)Arbitrary-View Human Action Recognition: A Varying-View RGB-D Action DatasetIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297584531:1(289-300)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.2975845
Ji YXu FYang YShen FShen HZheng WBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)A Large-scale RGB-D Database for Arbitrary-view Human Action RecognitionProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240675(1510-1518)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240675
Korpela JMaekawa T(2018)Privacy preserving recognition of object-based activities using near-infrared reflective markersPersonal and Ubiquitous Computing10.1007/s00779-017-1070-922:2(365-377)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s00779-017-1070-9
Herranz LJiang SXu R(2017)Modeling Restaurant Context for Food RecognitionIEEE Transactions on Multimedia10.1109/TMM.2016.261486119:2(430-440)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TMM.2016.2614861
Kojima SOhyama WWakabayashi T(2017)Gesture recognition based on spatiotemporal histogram of oriented gradient variation2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT)10.1109/ICIEV.2017.8338581(1-4)Online publication date: Sep-2017
https://doi.org/10.1109/ICIEV.2017.8338581
Cha EForlizzi JSrinivasa SAdams JSmart WMutlu BTakayama L(2015)Robots in the HomeProceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction10.1145/2696454.2696465(319-326)Online publication date: 2-Mar-2015
https://dl.acm.org/doi/10.1145/2696454.2696465
Ohyama WHotta SWakabayashi T(2015)Spatiotemporal auto-correlation of grayscale gradient with importance map for cooking gesture recognition2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)10.1109/ACPR.2015.7486487(166-170)Online publication date: Nov-2015
https://doi.org/10.1109/ACPR.2015.7486487
Bianco SCiocca GNapoletano PSchettini R(2015)An interactive tool for manual, semi-automatic and automatic video annotationComputer Vision and Image Understanding10.1016/j.cviu.2014.06.015131:C(88-99)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.cviu.2014.06.015
Hung NBao PKim J(2015)Gesture recognition in cooking video based on image features and motion features using Bayesian network classifierEmerging Trends in Image Processing, Computer Vision and Pattern Recognition10.1016/B978-0-12-802045-6.00024-7(379-392)Online publication date: 2015
https://doi.org/10.1016/B978-0-12-802045-6.00024-7
Sato AWatanabe KRekimoto JButz AGreenberg SBakker SLoke LDe Luca A(2014)MimiCookProceedings of the 8th International Conference on Tangible, Embedded and Embodied Interaction10.1145/2540930.2540952(121-124)Online publication date: 16-Feb-2014
https://dl.acm.org/doi/10.1145/2540930.2540952
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Multi-scenario gesture recognition using Kinect

Combining multiple depth-based descriptors for hand gesture recognition

Hand gesture recognition with depth data

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