Oregon State University

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PhD Final Oral Examination – Rob Hess


Thursday, June 14, 2012 11:00 AM - 1:00 PM

Computer Vision for Understanding American Football in Video
In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish a different sub-task within a larger system for annotating, interpreting, and analyzing collections of American football video. The analysis of football video is useful in its own right, as teams at all levels from high school to professional football currently spend thousands of dollars and countless human work hours processing video of their own play and the play of their opponents with the aim of developing strategy and improving performance. However, because football is an extremely challenging visual domain, with challenges ranging from the chaotic motion and identical appearance of the players to the visual clutter on the field in the form of logos and other markings, computer vision algorithms developed towards the end goal of understanding American football are broadly applicable across a variety of visual problems.

I address four specific football-related problems in this thesis. First, I describe an approach for registering video with a static model (i.e.\ the football field in the American football domain) using a novel concept of locally distinctive image feature matches. I also introduce a novel registration transform stability test, which we use to initialize our registration procedure.

Second, I outline a novel method for constructing mosaics from collections of video. This method takes a greedy utility maximization approach to build mosaics that achieve user-definable mosaic quality objectives. While broadly applicable, our mosaicing approach accomplishes several tasks specifically relevant to the analysis of football video, including automatically constructing reference image sets for our video registration procedure and for computing background models for initial formation recognition and player tracking algorithms.

Third, I present an approach for recognizing initial player formations. This approach, called the Mixture-of-Parts Pictorial Structure (MoPPS) model, extends classical pictorial structures to recognize multi-part objects whose parts can vary in both type and location and for which an object part's location can depend on its type. While this model is effective in the American football domain, it is also broadly applicable.

Finally, I address the problem of tracking football players through video using a novel particle filtering formulation and an associated discriminative training procedure that directly maximizes filter performance based on observed errors during tracking. This particle filtering framework and training procedure are also broadly applicable.

For each of the algorithms I describe, I also present a series of detailed experiments demonstrating the method's effectiveness in the American football domain. As a further contribution, I have made the data sets from most of these experiments publicly available.

Major Advisor: Alan Fern
Committee: Prasad Tadepalli
Committee: Sinisa Todorovic
Committee: Tom Dietterich
GCR: Ron Metoyer


Kearney Hall (campus map)
112
Shannon Thompson
1 541 737 7234
shannon.thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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