Abstract
The analysis and interpretation of video data is an important component of modern vision applications such as biometrics, surveillance, motionsynthesis and web-based user interfaces. A common requirement among these very different applications is the ability to learn statistical models of appearance and motion from a collection of videos, and then use them for recognizing actions or persons in a new video. These applications in video analysis require statistical inference methods to be devised on non-Euclidean spaces or more formally on manifolds. This chapter outlines a broad survey of applications in video analysis that involve manifolds. We develop the required mathematical tools needed to perform statistical inference on manifolds and show their effectiveness in real video-understanding applications.
This work was partially supported by the Office of Naval Research under the Grant n00014-09-10664.
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Turaga, P., Veeraraghavan, A., Srivastava, A., Chellappa, R. (2010). Statistical Analysis on Manifolds and Its Applications to Video Analysis. In: Schonfeld, D., Shan, C., Tao, D., Wang, L. (eds) Video Search and Mining. Studies in Computational Intelligence, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12900-1_5
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