Skip to main content
SpringerLink
Log in

MVSC-Bench: A Tool to Benchmark Classification Methods for Multivariate Spatiotemporal Data

  • Conference paper
  • First Online:
Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

Included in the following conference series:

  • 1421 Accesses

Abstract

Applications focusing on analysis of multivariate spatiotemporal series (MVS) have proliferated over the past decade. Researchers in a wide array of domains ranging from action recognition to sports analytics have come forward with novel methods to classify this type of data, but well-defined benchmarks for comparative evaluation of the MVS classification methods are non-existent. We present MVSC-Bench, to target this gap.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Raheja, J.L., Minhas, M., Prashanth, D., Shah, T., Chaudhary, A.: Robust gesture recognition using Kinect: a comparison between DTW and HMM. Optik Int. J. Light Electron Optics 126(11–12), 1098–1104 (2015)

    Article  Google Scholar 

  2. Kashani, F.B., Medioni, G., Nguyen, K., Nocera, L., Shahabi, C., Wang, R., Blanco, C.E., Chen, Y.-A., Chung, Y.-C., Fisher, B., Mulroy, S., Requejo, P., Winstein, C.: Monitoring mobility disorders at home using 3D visual sensors and mobile sensors. In: Proceedings of the 4th Conference on Wireless Health (WH 2013). ACM, New York (2013)

    Google Scholar 

  3. Gianaria, E., Grangetto, M., Lucenteforte, M., Balossino, N.: Human classification using gait features. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds.) Biometric Authentication, BIOMET 2014, vol. 8897. Springer, Cham (2014)

    Google Scholar 

  4. Sinha, A., Chakravarty, K.: Pose based person identification using kinect. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, pp. 497–503 (2013)

    Google Scholar 

  5. Araujo, R.M., Graña, G., Andersson, V.: Towards skeleton biometric identification using the microsoft kinect sensor. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC 2013). ACM, New York, pp. 21–26 (2013)

    Google Scholar 

  6. Andersson, V.O., Araujo, R.M.: Person identification using anthropometric and gait data from kinect sensor. In: Proceedings of the 29th AAAI Conference (2015)

    Google Scholar 

  7. Ramos, J.: Using TF-IDF to Determine Word Relevance in Document Queries (1999)

    Google Scholar 

  8. Pettersen, S.A., Johansen, D., Johansen, H., Berg-Johansen, V., Gaddam, V.R., Mortensen, A., Langseth, R., Griwodz, C., Stensland, H.K., Halvorsen, P.: Soccer video and player position dataset. In: Proceedings of the 5th ACM Multimedia Systems Conference (MMSys 2014). ACM, New York, pp. 18–23 (2014)

    Google Scholar 

  9. Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Process. 18(8), 1905–1910 (2009)

    Article  MathSciNet  Google Scholar 

  10. Morse, M.D., Patel, J.M.: An efficient and accurate method for evaluating time series similarity. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (SIGMOD 2007). ACM, New York, pp. 569–580 (2007)

    Google Scholar 

  11. Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proceedings 15th International Conference on Data Engineering (Cat. No. 99CB36337), Sydney, NSW, pp. 106–115 (1999)

    Google Scholar 

  12. Kulkarni, S.: siddhantkulkarni/MVSClassification. GitHub (2017). https://github.com/siddhantkulkarni/MVSClassification. Accessed 26 Mar 2017

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA

    Siddhant Kulkarni & Farnoush Banaei-Kashani

Authors
  1. Siddhant Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farnoush Banaei-Kashani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siddhant Kulkarni .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kulkarni, S., Banaei-Kashani, F. (2017). MVSC-Bench: A Tool to Benchmark Classification Methods for Multivariate Spatiotemporal Data. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64367-0_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation