skip to main content
10.1145/3319921.3319967acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiciaiConference Proceedingsconference-collections
research-article

False Positive Eliminating Using Frame Association Information

Published: 15 March 2019 Publication History

Abstract

As a fundamental technology of human motion analysis, video based pose estimation has attracted more and more attention of researchers. While most mainstream methods treated videos as a collection of unrelated frames, random false positives caused by light and shadow changes will bring greater uncertainty to the results of human body estimation. To eliminate these false positives in multi-person 2D pose estimation, a method of multi-person 2D pose estimation using the time-space relationship of adjacent frames is proposed. At first, Frame Association Information(FAI) is defined to represent the relationship between person objects in adjacent frames, which contents similarity of position, size and poses. Then, a method of false positives eliminating is designed using FAIs between frames, which can identify and remove false positives in frames according to their similarities to human objects in a benchmark frame. The following experiments show the effectiveness of our method.

References

[1]
M. Andriluka, L. Pishchulin, P. Gehler, B. Schiele, C. 2014. 2D human pose estimation: New benchmark and state of the art analysis. In CVPR 2014(Columbus, OH, USA, June 23-28), 3686--3693.
[2]
L. Pishchulin and M. Andriluka, P. Gehler, B. Schiele, C. 2013. Poselet conditioned pictorial structures. In CVPR 2013(Portland, OR, June), 588--595.
[3]
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele, C. 2013. Strong appearance and expressive spatial models for human pose estimation. In ICCV 2013(Sydney, NSW, Australia, Dec 1-8), 3487--3494.
[4]
Y. Yang and D. Ramanan, C. 2011. Articulated pose estimation with flexible mixtures-of-parts. In CVPR 2011(Colorado Springs, CO, USA, USA, June 20-25), 1385--1392.
[5]
M. Sun and S. Savarese, C. 2011. Articulated part-based model for joint object detection and pose estimation. In ICCV 2011(Barcelona, Spain, Nov 6-13), 723--730.
[6]
Y. Tian, C. L. Zitnick, S. G. Narasimhan, C. 2012. Exploring the spatial hierarchy of mixture models for human pose estimation. In ECCV 2012(Florence, Italy, Oct 7-13), 256--269.
[7]
M. Dantone, J. Gall, C. Leistner, and L. Van Gool, C. 2013. Human pose estimation using body parts dependent joint regressors. In CVPR 2013(Portland, OR, USA, June 23-28), 3041--3048.
[8]
L. Karlinsky and S. Ullman, C. 2012. Using linking features in learning non-parametric part models. In ECCV 2012(Florence, Italy, Oct 7-13), 326--339.
[9]
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik, C. 2016. Human pose estimation with iterative error feedback. In CVPR 2016(Las Vegas, NV, USA, June 27-30), 4733--4742.
[10]
T. Pfister, J. Charles, and A. Zisserman, C. 2015. Flowing convents for human pose estimation in videos. In ECCV 2015(Santiago, Chile, Dec 7-13), 1913--1921.
[11]
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. Gehler, and B. Schiele, C. 2016. Deepcut: Joint subset partition and labeling for multi person pose estimation. In CVPR 2016(Las Vegas, NV, USA, June 27-30), 4929--4937.
[12]
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, C. 2015. Efficient object localization using convolutional networks. In CVPR 2015(Boston, Massachusetts, USA, June 7-12), 648--656.
[13]
S.E. Wei, V. Ramakrishna, T. Kanade, Y. Sheikh, C. 2016. Convolutional pose machines. In CVPR 2016(Las Vegas, NV, USA, June 27-30), 4724--4732.
[14]
A. Newell, K. Yang, and J. Deng, C. 2016. Stacked hourglass networks for human pose estimation. In ECCV 2016(Amsterdam, The Netherlands, Oct 11-14), 483--499.
[15]
E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele, C. 2016. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In ECCV 2016(Amsterdam, The Netherlands, Oct 11-14), 34--50.
[16]
Zhe. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, C. 2017. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR 2017(Honolulu, HI, USA, July 21-26), 1302--1310.
[17]
G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy, C. 2017. Towards accurate multi-person pose estimation in the wild. In CVPR 2017(Honolulu, HI, USA, July 21-26), 3711--3719.
[18]
F. S. Khan, J. van de Weijer, and M. Vanrell. 2012. Modulating shape features by color attention for object recognition. J. International Journal of Computer Vision.(May, 2012), 49--64.

Index Terms

  1. False Positive Eliminating Using Frame Association Information

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence
    March 2019
    279 pages
    ISBN:9781450361286
    DOI:10.1145/3319921
    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

    • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
    • University of Texas-Dallas: University of Texas-Dallas

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 March 2019

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Human pose estimation
    2. false positives eliminating
    3. frame association information
    4. local video
    5. multi-person

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • National Natural Science Foundation of China
    • Liaoning Province Doctor Startup Fund
    • Program for Changjiang Scholars and Innovative Research Team in University
    • Program for the Liaoning Distinguished Professor, Program forDalian High-level Talent?s Innovation
    • Innovation Fund Plan for Dalian Science and Technology

    Conference

    ICIAI 2019

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 58
      Total Downloads
    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 13 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