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)
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-12061 |
| Date | January 2009 |
| Creators | Launila, Andreas |
| Publisher | KTH, Datorseende och robotik, CVAP |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-CSC-E, ISSN-1653-5715 ; 2009:130 |
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