Dominant Motion Estimation from Two Images
Date Issued
2009
Date
2009
Author(s)
Huang, Po-Yu
Abstract
Video motion analysis is a critical issue for multimedia signal processing. It can help video summarization and classification, which make the browsing and indexing more quickly. Clustering motions under fixed cameras or calibrated cameras has been developed recently. The drawback of these methods is it’s hard to realize on real video, which is usually un-calibrated. To deal with this problem, we propose a Hough transform based method which samples 3D motion parameters, including the focal length of the camera intrinsic matrix, the relative rotation, and translation. However, fully sampling all parameters is time consuming. We partially sample the focal length of the first camera and relative rotation. Then, we obtain the remaining parameters by solving a geometry optimization problem. The selection of multiple motion models is defined by a voting strategy. In addition, we combine a branch and bound algorithm with Hough transformation to solve high computation complexity of traditional Hough transform. The algorithm reduces sampling rotation space to its subspace by an efficient rotation space searching. The results of motion models are used to identify the dominant motion in videos.
Subjects
Motion Estimation
Computer vision
Type
thesis
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