Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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It is fairly common in video sequences that a mostly fixed background (scene) is imaged with or without objects. The dominant background changes in the image plane mostly due to camera operations and motion (zoom, pan, tilt, track etc.). We address the problem of computation of the dominant image transformation over time and demonstrate how this can be effectively used for efficient video representation through video mosaicing and image registration. We formulate the problem of dominant component estimation as that of model based robust estimation using M estimators with direct, multi resolution methods. In addition to 2D affine and plane projective models, that have been used in the past for describing image motion using direct methods, we also employ a true 3D model of motion and scene structure imaged with uncalibrated cameras. This model parameterizes the image motion as that due to a planar component and a parallax component. For rigid 3D scenes imaged under camera motion only, least squares (LS) methods with the plane and parallax parameterization are also presented. Furthermore, in the context of robust estimation, in contrast with previous approaches for similar problems, our algorithm employs an automatic computation of a scale parameter that is crucial in rejecting the non dominant components as outliers.