Motion segmentation by tracking edge information over multiple frames

Paul Smith, Tom Drummond, Roberto Cipolla

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

This paper presents a new Bayesian framework for layered motion segmentation, dividing the frames of an image sequence into foreground and background layers by tracking edges. The first frame in the sequence is segmented into regions using image edges, which are tracked to estimate two affine motions. The probability of the edges fitting each motion is calculated using 1st order statistics along the edge. The most likely region labelling is then resolved using these probabilities, together with a Markov Random Field prior. As part of this process one of the motions is also identified as the foreground motion. Good results are obtained using only two frames for segmentation. However, it is also demonstrated that over multiple frames the probabilities may be accumulated to provide an even more accurate and robust segmentation. The final region labelling can be used, together with the two motion models, to produce a good segmentation of an extended sequence.

Original languageEnglish
Title of host publicationComputer Vision - 6th European Conference on Computer Vision, ECCV 2000, Proceedings
PublisherSpringer-Verlag London Ltd.
Pages396-410
Number of pages15
ISBN (Print)3540676864
Publication statusPublished - 1 Jan 2000
Externally publishedYes
EventEuropean Conference on Computer Vision 2000 - Dublin, Ireland
Duration: 26 Jun 20001 Jul 2000
Conference number: 6th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1843
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Computer Vision 2000
Abbreviated titleECCV 2000
CountryIreland
CityDublin
Period26/06/001/07/00

Cite this

Smith, P., Drummond, T., & Cipolla, R. (2000). Motion segmentation by tracking edge information over multiple frames. In Computer Vision - 6th European Conference on Computer Vision, ECCV 2000, Proceedings (pp. 396-410). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1843). Springer-Verlag London Ltd..