Motion Estimation and Segmentation of Natural Phenomena

Da Chen, Wenbin Li, Peter Hall

Research output: Contribution to conference › Paper

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Abstract

Dense motion estimation for dynamic natural phenomena (water, smoke, fire, etc.) is a significant open problem. Current approaches tend to be either general, giving poor results, or specialise in one phenomenon and fail to generalise. Segmentation of phenomena is also an open problem. This paper describes an approach to estimate dense motion for dynamic phenomena that is simple, general, and which yields state of the art results. We use our dense motion field to segment phenomena to above state of the art levels. We demonstrate our contributions using lab-based video, video from a public dataset, and from the internet.
Original languageEnglish
Publication statusPublished - 4 Sep 2018
Event29th British Machine Vision Conference 2018 - Northumbria University, Newcastle
Duration: 3 Sep 2018 → 6 Sep 2018
http://bmvc2018.org/

Conference

Conference29th British Machine Vision Conference 2018
Abbreviated titleBMVC 2018
CityNewcastle
Period3/09/18 → 6/09/18
Internet address

Keywords

  • natutal phenomena, tracking, segmentation

Access to Document

    Projects

    Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

    Cosker, D., Campbell, N., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., O'Neill, E., Richardt, C. & Yang, Y.

    Engineering and Physical Sciences Research Council

    1/09/15 → 28/02/21

    Project: Research council

    • Cite this

    Chen, D., Li, W., & Hall, P. (2018). Motion Estimation and Segmentation of Natural Phenomena. Paper presented at 29th British Machine Vision Conference 2018, Newcastle, .