research-article

RGB-D action recognition using linear coding

Authors:

Huaping Liu,

Mingyi Yuan,

Fuchun SunAuthors Info & Claims

Neurocomputing, Volume 149, Issue PA

Pages 79 - 85

https://doi.org/10.1016/j.neucom.2013.12.061

Published: 03 February 2015 Publication History

Abstract

In this paper, we investigate action recognition using an inexpensive RGB-D sensor (Microsoft Kinect). First, a depth spatial-temporal descriptor is developed to extract the interested local regions in depth image. Such descriptors are very robust to the illumination and background clutter. Then the intensity spatial-temporal descriptor and the depth spatial-temporal descriptor are combined and feeded into a linear coding framework to get an effective feature vector, which can be used for action classification. Finally, extensive experiments are conducted on a publicly available RGB-D action recognition dataset and the proposed method shows promising results.

References

[1]

B. Ni, G. Wang, M. Pierre, RGBD-HuDaAct: a color-depth video database for human daily activity recognition, in: IEEE International Conference on Computer Vision Workshop, IEEE, Barcelona, Spain 2011, pp. 1147-1153.

Google Scholar

[2]

J. Sung, C. Ponce, B. Selman, A. Saxena, Unstructured human activity detection from RGBD images, in: IEEE International Conference on Robotics and Automation, IEEE, St. Paul, MN, USA 2012, pp. 842-849.

Google Scholar

[3]

W. Li, Z. Zhang, Z. Liu, Action recognition based on a bag of 3d points, in: IEEE Conference on Computer Vision and Pattern Recognition Workshop, 2010.

Google Scholar

[4]

L. Morency, A. Quattoni, T. Darrell, Latent-dynamic discriminative models for continuous gesture recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Minneapolis, USA 2007, pp. 1-8.

Google Scholar

[5]

I. Laptev, On space-time interest points, Int. J. Comput. Vis., 64 (2005) 107-123.

Digital Library

Google Scholar

[6]

N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, IEEE, San Diego, USA 2005, pp. 886-893.

Digital Library

Google Scholar

[7]

N. Dalal, B. Triggs, C. Schmid, Human detection using oriented histograms of flow and appearance, in: European Conference on Computer Vision, Springer, Graz, Austria 2006, pp. 428-441.

Crossref

Google Scholar

[8]

A. Jain, M. Murty, P. Flynn, Data clustering, ACM Comput. Surv., 31 (1999) 264-323.

Digital Library

Google Scholar

[9]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, San Francisco, CA, USA 2010, pp. 3360-3367.

Google Scholar

[10]

L. Fei-Fei, P. Perona, A Bayesian hierarchical model for learning natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, San Diego, USA 2005, pp. 524-531.

Digital Library

Google Scholar

[11]

H. Wang, M. Ullah, A. Klaser, I. Laptev, C. Schmid, Evaluation of local spatio-temporal features for action recognition, in: British Machine Vision Conference, 2009.

Crossref

Google Scholar

[12]

S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, New York, NY, USA 2006, pp. 2169-2178.

Digital Library

Google Scholar

[13]

Public STIPs Binaries, {http://www.di.ens.fr/~laptev/download.html}, 2005.

Google Scholar

[14]

C. Chang, C. Lin, Libsvm, ACM Trans. Intell. Syst. Technol., 2 (2011) 27.

Digital Library

Google Scholar

Cited By

View all

Guo ZHou YWang PGao ZXu MLi W(2022)FT-HID: a large-scale RGB-D dataset for first- and third-person human interaction analysisNeural Computing and Applications10.1007/s00521-022-07826-w35:2(2007-2024)Online publication date: 7-Oct-2022
https://dl.acm.org/doi/10.1007/s00521-022-07826-w
Zhang HZhong PHe JXia C(2017)Combining depth-skeleton feature with sparse coding for action recognitionNeurocomputing10.1016/j.neucom.2016.12.041230:C(417-426)Online publication date: 22-Mar-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.12.041
Li GZhang XZheng SLi D(2017)Semi-supervised convex nonnegative matrix factorizations with graph regularized for image representationNeurocomputing10.1016/j.neucom.2016.04.028237:C(1-11)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.04.028
Show More Cited By

RGB-D action recognition using linear coding
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

Recommendations

A local descriptor based on Laplacian pyramid coding for action recognition

We present a new descriptor for local representation of human actions. In contrast to state-of-the-art descriptors, which use spatio-temporal features to describe cuboids detected from video sequences, we propose to employ a 2D descriptor based on the ...
Weighted feature trajectories and concatenated bag-of-features for action recognition

Key-point trajectory based approaches to recognizing human actions in realistic videos have recently shown promising results. However, their coverage of the entire actor is not sufficient for describing human actions, and the trajectories often ...
Local feature coding for action recognition using RGB-D camera
ISNN'13: Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I

In this paper, we perform activity recognition using an inexpensive RGBD sensor (Microsoft Kinect). The main contribution of this paper is that the conventional STIPs feature are extracted from not only the RGB image, but also the depth image. To the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Neurocomputing Volume 149, Issue PA

February 2015

472 pages

ISSN:0925-2312

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 03 February 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Guo ZHou YWang PGao ZXu MLi W(2022)FT-HID: a large-scale RGB-D dataset for first- and third-person human interaction analysisNeural Computing and Applications10.1007/s00521-022-07826-w35:2(2007-2024)Online publication date: 7-Oct-2022
https://dl.acm.org/doi/10.1007/s00521-022-07826-w
Zhang HZhong PHe JXia C(2017)Combining depth-skeleton feature with sparse coding for action recognitionNeurocomputing10.1016/j.neucom.2016.12.041230:C(417-426)Online publication date: 22-Mar-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.12.041
Li GZhang XZheng SLi D(2017)Semi-supervised convex nonnegative matrix factorizations with graph regularized for image representationNeurocomputing10.1016/j.neucom.2016.04.028237:C(1-11)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.04.028
Ji XCheng JTao DWu XFeng W(2017)The spatial Laplacian and temporal energy pyramid representation for human action recognition using depth sequencesKnowledge-Based Systems10.1016/j.knosys.2017.01.035122:C(64-74)Online publication date: 15-Apr-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.01.035
Sun QLiu HMa LZhang T(2016)A novel hierarchical Bag-of-Words model for compact action representationNeurocomputing10.1016/j.neucom.2015.09.074174:PB(722-732)Online publication date: 22-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.09.074
D'Orazio TMarani RRenò VCicirelli G(2016)Recent trends in gesture recognitionImage and Vision Computing10.1016/j.imavis.2016.05.00752:C(56-72)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.imavis.2016.05.007

Abstract

References

Cited By

Recommendations

A local descriptor based on Laplacian pyramid coding for action recognition

Weighted feature trajectories and concatenated bag-of-features for action recognition

Local feature coding for action recognition using RGB-D camera

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations