Skip to main content
SpringerLink
Log in

A Combo Object Model for Maritime Boat Ramps Traffic Monitoring

  • Conference paper
  • First Online:
Book cover Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

Included in the following conference series:

Abstract

Conventional tracking methods are incapable of tracking boats towed by vehicles on boat ramps because the relative geometry of these combined objects changes as they move up and down the ramp. In the context of maritime boat ramp surveillance, fishing trailer boat is the object of interest for monitoring the amount of recreational fishing activities over the time. Instead of tracking trailer boat as a single object, this paper proposes a novel boat-vehicle combo object model, by which each boat is tracked as a combination of a trailered boat and a towing vehicle, and the relationship between these two components is modelled in multi-feature space and traced across consecutive frames. Experimental results show that the proposed combo modelling tracks the object of interest accurately and reliably in real-world boat traffic videos.

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. Khan, Z., Balch, T., Dellaert, F.: Mcmc-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1918 (2005)

    Article  Google Scholar 

  2. Porikli, F., Tuzel, O.: Object tracking in low-frame-rate video. In: Image and Video Communications and Processing, vol. 72, 6 April 2005

    Google Scholar 

  3. Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1728–1740 (2008)

    Article  Google Scholar 

  4. Chuang, M., Hwang, J., Williams, K., Towler, R.: Tracking live fish from low-contrast and low-frame-rate stereo videos. IEEE Trans. Circuits Syst. Video Technol. 25(1), 167–179 (2015)

    Article  Google Scholar 

  5. Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1820–1833 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Unitec Institute of Technology, 139 Carrington Road, Mount Albert, Auckland, 1025, New Zealand

    Jing Zhao, Shaoning Pang & Abdolhossein Sarrafzadeh

  2. The National Institute of Water and Atmospheric Research, 41 Market Place, Viaduct Harbour, Auckland, New Zealand

    Bruce Hartill

Authors
  1. Jing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaoning Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bruce Hartill
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdolhossein Sarrafzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoning Pang .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Zhao, J., Pang, S., Hartill, B., Sarrafzadeh, A. (2016). A Combo Object Model for Maritime Boat Ramps Traffic Monitoring. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_69

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46687-3_69

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation