Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Optical Flow Features for Event Detection
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis, we employ optical flow features for the detection of the rigid or non‐rigid single object on an input video. For optical flow estimation, we use the Point Line [PL] method [2] (as a local method) to estimate the motion of the image sequence which is generated from the input video stream. Although the Lukas and Kanade [LK] is a popular local method for estimation of the optical flow, it is weak in dealing with the linear symmetric images even by use of regularization [e.g. Tikhonov]. The PL method is more powerful than the LK method and can properly separate both line flow and point flow. For dealing with rapidly changing data in some part of an image (high motion problem), a gaussian pyramid with five levels (different image resolutions) is employed. In this way, the pyramid height (Level) must be chosen properly according to the maximum optical flow that we expect in each section of the image without iteration. After determining the best‐estimated optical flow vector for every pixel, the algorithm should detect an object on video with its direction to the right or left. By using techniques such as segmentation and averaging the magnitude of flow vectors the program can detect and distinguish rigid objects (e.g. a car) and non‐rigid objects (e.g. a human). Finally the algorithm makes a new video output that includes detected object with flow vectors, the pyramid levels map which has been used for optical flow estimation and a respective binary image.

Place, publisher, year, edition, pages
2014. , 67 p.
Keyword [en]
optical flow, event detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-25016Local ID: IDE1324OAI: oai:DiVA.org:hh-25016DiVA: diva2:711638
Subject / course
Computer science and engineering
Presentation
2013-12-19, 08:00 (English)
Supervisors
Examiners
Available from: 2014-04-11 Created: 2014-04-10 Last updated: 2014-04-11Bibliographically approved

Open Access in DiVA

Final thesis report(5907 kB)510 downloads
File information
File name FULLTEXT01.pdfFile size 5907 kBChecksum SHA-512
0bd7d5390dd67eb470a1e3f1d7407aca35bb6cf656a00ed316fa206941b9b7f87f694230d527628ccc5ddb765b1830a4c083a004fdcc6bc178f74e348c079aa5
Type fulltextMimetype application/pdf

By organisation
School of Information Science, Computer and Electrical Engineering (IDE)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 510 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 434 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf