skip to main content
10.1145/3301506.3301517acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicvipConference Proceedingsconference-collections
research-article

Kick Recognition System Using Dual RGB-D Cameras

Published: 29 December 2018 Publication History

Abstract

This paper introduces a kick recognition system based on human body detection using dual RGB-D cameras. Recently, the availability of RGB-D cameras makes it possible to get the human body joints that are informative for activity analysis. However, single camera-based approaches enforce frontal-oriented action due to the occlusion problem. Using dual RGB-D cameras and a smart sandbag, the proposed system detects major joints and recognizes various kick actions from a general user in real time. For each camera, our system detects salient body parts in a kick action such as a head and feet. A local detector trained with a supervised model is used for the head detection. The detected body parts are converted into a quadtree-structured graph model to detect feet using accumulative geodesic distance (AGD). To deal with the occlusion by the sandbag, our system compares the result of each camera and selects the most reliable one based on AGD. For the kick recognition, a finite-state machine is adopted to track and to segment continuous kick movements into different states. Considering a viewpoint change and a variable kick speed, fixed size descriptors are constructed from the interpolated action to recognize user kicks. We evaluated our system using various kick actions in taekwondo and achieved a high recognition rate of 92%.

References

[1]
Baak, A. et al. A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera. 2011 IEEE International Conference on Computer Vision, (Nov. 2011) 1092--1099.
[2]
Baek, S. and Kim, M. 2015. Dance Experience System Using Multiple Kinects. International Journal of Future Computer and Communication. 4, 1 (Feb. 2015), 45--49.
[3]
Chen, H. et al. 2016. A novel hierarchical framework for human action recognition. Pattern Recognition. 55, (Jul. 2016), 148--159.
[4]
Comaniciu, D. et al. 2000. Real-time tracking of non-rigid objects using mean shift. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) (Hilton Head Island, SC, USA, 2000), 142--149.
[5]
Dalal, N. and Triggs, B. 2005. Histograms of Oriented Gradients for Human Detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (San Diego, CA, USA, 2005), 886--893.
[6]
Hong, S. and Kim, M. 2016. A Framework for Human Body Parts Detection in RGB-D Image. Journal of Korea Multimedia Society. 19, 12 (Dec. 2016), 1927--1935.
[7]
Kapsouras, I. and Nikolaidis, N. 2014. Action recognition on motion capture data using a dynemes and forward differences representation. Journal of Visual Communication and Image Representation. 25, 6 (Aug. 2014), 1432--1445.
[8]
Kim, Y. et al. 2017. Motion Capture of the Human Body Using Multiple Depth Sensors. ETRI Journal. 39, 2 (Apr. 2017), 181--190.
[9]
Moon, G. et al. 2017. Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation. arXiv:1706.04758 {cs}. (Jun. 2017).
[10]
Moon, S. et al. 2016. Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering. International Journal of Advanced Robotic Systems. 13, 2 (Mar. 2016), 65.
[11]
Nishi, K. and Miura, J. 2017. Generation of human depth images with body part labels for complex human pose recognition. Pattern Recognition. (Jun. 2017).
[12]
Ofli, F. et al. 2014. Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition. Journal of Visual Communication and Image Representation. 25, 1 (Jan. 2014), 24--38.
[13]
Plagemann, C. et al. 2010. Real-time identification and localization of body parts from depth images. 2010 IEEE International Conference on Robotics and Automation (Anchorage, AK, May 2010), 3108--3113.
[14]
Shotton, J. et al. 2011. Real-time human pose recognition in parts from single depth images. Computer Vision and Pattern Recognition. CVPR 2011 (June. 2011).
[15]
Yang, X. and Tian, Y.L. 2012. EigenJoints-based action recognition using Naive-Bayes-Nearest-Neighbor. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (Providence, RI, USA, Jun. 2012), 14--19.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICVIP '18: Proceedings of the 2018 2nd International Conference on Video and Image Processing
December 2018
252 pages
ISBN:9781450366137
DOI:10.1145/3301506
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]

In-Cooperation

  • Kyoto University: Kyoto University
  • TU: Tianjin University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 December 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Human kick recognition
  2. RGB-D cameras
  3. body joint detection
  4. feature representation
  5. sandbag experience system

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Conference

ICVIP 2018

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 73
    Total Downloads
  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media