Skip to main content
SpringerLink
Log in

Optical Flow and Trajectory Estimation Methods

  • Book
  • © 2016

Overview

Authors:
  1. Joel Gibson

    1. Blackmagic Design, Colorado Springs, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Oge Marques

    1. Department of Computer and Electrical Engineering, Florida Atlantic University, Boca Raton, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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

Access this book

Log in via an institution
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

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (4 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Optical Flow Fundamentals

    • Joel Gibson, Oge Marques
    Pages 1-7

  3. Optical Flow and Trajectory Methods in Context

    • Joel Gibson, Oge Marques
    Pages 9-23

  4. Sparse Regularization of TV-L\(^1\) Optical Flow

    • Joel Gibson, Oge Marques
    Pages 25-40

  5. Robust Low Rank Trajectories

    • Joel Gibson, Oge Marques
    Pages 41-49
Back to top

Keywords

About this book

This brief focuses on two main problems in the domain of optical flow and trajectory estimation: (i) The problem of finding convex optimization methods to apply sparsity to optical flow; and (ii) The problem of how to extend sparsity to improve trajectories in a computationally tractable way.


Beginning with a review of optical flow fundamentals, it discusses the commonly used flow estimation strategies and the advantages or shortcomings of each. The brief also introduces the concepts associated with sparsity including dictionaries and low rank matrices. Next, it provides context for optical flow and trajectory methods including algorithms, data sets, and performance measurement. The second half of the brief covers sparse regularization of total variation optical flow and robust low rank trajectories. The authors describe a new approach that uses partially-overlapping patches to accelerate the calculation and is implemented in a coarse-to-fine strategy. Experimental results show that combining total variation and a sparse constraint from a learned dictionary is more effective than employing total variation alone.


The brief is targeted at researchers and practitioners in the fields of engineering and computer science. It caters particularly to new researchers looking for cutting edge topics in optical flow as well as veterans of optical flow wishing to learn of the latest advances in multi-frame methods.




Authors and Affiliations

  • Blackmagic Design, Colorado Springs, USA

    Joel Gibson

  • Department of Computer and Electrical Engineering, Florida Atlantic University, Boca Raton, USA

    Oge Marques

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation