Joint Random Sample Consensus and Multiple Motion Models for Robust Video Tracking

Petter Strandmark, Irene Gu

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

9 Citeringar (SciVal)

Sammanfattning

We present a novel method for tracking multiple objects in video captured by a non-stationary camera. For low quality video, RANSAC estimation fails when the number of good matches shrinks below the minimum required to estimate the motion model. This paper extends RANSAC in the following ways: (a) Allowing multiple models of different complexity to be chosen at random; (b) Introducing a conditional probability to measure the suitability of each transformation candidate, given the object locations in previous frames; (c) Determining the best suitable transformation by the number of consensus points, the probability and the model complexity. Our experimental results have shown that the proposed estimation method better handles video of low quality and that it is able to track deformable objects with pose changes, occlusions, motion blur and overlap. We also show that using multiple models of increasing complexity is more effective than just using RANSAC with the complex model only.
Originalspråkengelska
Titel på värdpublikationLecture Notes in Computer Science
FörlagSpringer
Sidor450-459
Volym5575/2009
DOI
StatusPublished - 2009
EvenemangScandinavian Conference on Image Analysis (SCIA) - Oslo, Norge
Varaktighet: 2009 juni 15 → …

Publikationsserier

Namn
Volym5575/2009
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

Konferens

KonferensScandinavian Conference on Image Analysis (SCIA)
Land/TerritoriumNorge
OrtOslo
Period2009/06/15 → …

Ämnesklassifikation (UKÄ)

  • Datorseende och robotik (autonoma system)
  • Matematik

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