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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 1 to 2 of 2 (0.111 seconds)

Spelling suggestions: "subject:"coarse heat post estimation"" "subject:"coarse held post estimation""

1

Real-Time Head Pose Estimation in Low-Resolution Football Footage / Realtidsestimering av huvudets vridning i lågupplösta videosekvenser från fotbollsmatcher

Launila, Andreas January 2009 (has links)
<p>This report examines the problem of real-time head pose estimation in low-resolution football footage. A method is presented for inferring the head pose using a combination of footage and knowledge of the locations of the football and players. An ensemble of randomized ferns is compared with a support vector machine for processing the footage, while a support vector machine performs pattern recognition on the location data. Combining the two sources of information outperforms either in isolation. The location of the football turns out to be an important piece of information.</p> / QC 20100707 / Capturing and Visualizing Large scale Human Action (ACTVIS)
head pose estimation
football
real-time
coarse head pose estimation
machine learning
computer vision
svm
randomized ferns
Computer science
Datalogi
Image analysis
Bildanalys
Computer engineering
Datorteknik
2

Real-Time Head Pose Estimation in Low-Resolution Football Footage / Realtidsestimering av huvudets vridning i lågupplösta videosekvenser från fotbollsmatcher

Launila, Andreas January 2009 (has links)
This report examines the problem of real-time head pose estimation in low-resolution football footage. A method is presented for inferring the head pose using a combination of footage and knowledge of the locations of the football and players. An ensemble of randomized ferns is compared with a support vector machine for processing the footage, while a support vector machine performs pattern recognition on the location data. Combining the two sources of information outperforms either in isolation. The location of the football turns out to be an important piece of information. / QC 20100707 / Capturing and Visualizing Large scale Human Action (ACTVIS)
head pose estimation
football
real-time
coarse head pose estimation
machine learning
computer vision
svm
randomized ferns
Computer Sciences
Datavetenskap (datalogi)
Computer Engineering
Datorteknik

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