Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 1, pp. 133--146, Feb. 2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....These segments are condensed into one or a few representative frames (key frames) 2] 13] to yield a pictorial summary of the video. Semantic video indexing based on object segmentation is discussed in [8] 14] Object and camera motion can also be used for analyzing and annotating video [3] [11]. Video annotation is often facilitated by prior knowledge of some general structure for the class of video under study. A basketball annotation system is discussed in [11] Since most of the video streams that modern digital storage systems have to deal with are available in the MPEG compressed ....

....segmentation is discussed in [8] 14] Object and camera motion can also be used for analyzing and annotating video [3] 11] Video annotation is often facilitated by prior knowledge of some general structure for the class of video under study. A basketball annotation system is discussed in [11]. Since most of the video streams that modern digital storage systems have to deal with are available in the MPEG compressed format, no content related operations are possible on these streams directly. The analysis of compressed video can proceed in one of the two fundamental ways. The first is ....

Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge. Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation. IEEE 10(1):133 -- 146, February 2000.

....of is chosen between 5 to 15 percent of the maximum error. 6 Camera Motion Estimation Under the assumption that the camera is undergoing rotation and zoom but no translation, the change of image intensity between frames can be modeled by the following 6 parameter projective transformation [8]. 3) 4) here, are the camera motion parameters and are the image coordinates of the corresponding points in two neighboring frames with respect to the standard orthogonal set of axes with origin at the image ....

....the standard orthogonal set of axes with origin at the image center. Suppose the camera undergoes small rotations ) about the camera axes and the focal length changes from to 6 between two consecutive frames, then the parameters satisfy [8]. 6 16 ) 6 ( 6 (5) If we assume that the perspective distortion effects are negligible, then the parameters can be set to zero. Now the rest of the parameters 3 are estimated by the following weighted least ....

Y. P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge. Rapid estimation of camera motion from compresses video with applications to video annotation. IEEE Trans. on Circuits and Systems for Video Technology, 10(1):133--146, Feb. 2000. (b)

....of the camera in the 3 D space, with some kind of projection that maps the object onto the camera s image plane. They estimate the parameters involved in the projected model, and the camera operations can be derived from these parameters. Usually some iterative algorithm is applied. Tan et al. [56, 57] propose a camera motion estimation method based on the physical model of the camera and 3 D coordinate transforms. The camera operation is modeled as a combination of rotations about the three axes and a translation of the coordinates. The zoom operation is modeled as a scaling function of object ....

....small (or a high sampling rate) and (2) the translation is minimal (or a high sampling rate) the model can be reduced to a 6 paramter one. In [56] the parameters are initially estimated using 4 pairs of pixel correspondences, and then refined using a recursive algorithm (the Kalman filter) In [57], motion vectors of compressed video are used as correspondences. The mean and variance of the estimated prediction error is calculated. Motion vectors that fall beyond a certain range are declared as outliers, and are excluded in the next iteration of the parameter estimation process. Experiments ....

[Article contains additional citation context not shown here]

Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Trans. on Circuits and Systems for Video Technology, 1999.

....color and motion features. The proposed clustering alg orithm is unsupervised, the number of clusters is determined automatically by the cluster validity analysis [3] Supervised version of clustering alg#6]1 hms by Hidden Markov Models can be found in [2, 6] and by decision rules can be found in [8]. In this paper, we focus on org#1 izing the content of video. Our approach is based on the pattern analysis and processing of temporal slices. We utilize the tensor histog#8] introduced byNg o et al. 4] for motion feature extraction and foreg#WS nd object seg# entation. By incorporating ....

Y.P.Tan,D.D.Saur,S.R.Kulkarni,andP.J. Ramadg e. Rapid estimation of camera motion from compressed video with application to video annotation. IEEE Trans. on Circuits and Systems for Video Technology, 10(1):133--146, Feb 2000.

....of the camera in the 3 D space, with some kind of projection that maps the object onto the camera s image plane. They estimate the parameters involved in the projected model, and the camera operations can be derived from these parameters. Usually some iterative algorithm is applied. Tan et al. [56, 57] propose a camera motion estimation method based on the physical model of the camera and 3 D coordinate transforms. The camera operation is modeled as a combination of rotations about the three axes and a translation of the coordinates. The zoom operation is modeled as a scaling function of object ....

....small (or a high sampling rate) and (2) the translation is minimal (or a high sampling rate) the model can be reduced to a 6 paramter one. In [56] the parameters are initially estimated using 4 pairs of pixel correspondences, and then refined using a recursive algorithm (the Kalman filter) In [57], motion vectors of compressed video are used as correspondences. The mean and variance of the estimated prediction error is calculated. Motion vectors that fall beyond a certain range are declared as outliers, and are excluded in the next iteration of the parameter estimation process. Experiments ....

[Article contains additional citation context not shown here]

Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Trans. on Circuits and Systems for Video Technology, 1999.

....detected dissolve whether its boundary frames qualify for a hard cut after its removal from the video sequence (see [6] If it does not qualify, then the detect dissolve is discard. In addition, we also noticed that dominant camera motion operations such as pans and zooms cause many false alarms [16,17,18]. Thus, all detected dissolves which temporally overlap by more than 70 with a strong dominant camera motion should be discarded, too. In an initial implementation, these two criteria helped to reduce the false alarm rate and were applied on each scale. The output of the post filtering stage is ....

Yap-Peng Tan. D.D. Saur, S.R. Kulkami, P.J. Ramadge. Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation. Circuits and Systems for Video Technology, IEEE Transactions on Volume: 10 1 , Feb. 2000 , Page(s): 133-146.

....and its velocity is bounded, we only need to refine the initial estimate s t = s t #t . If the video to be interpolated is in MPEG format, an alternative initial estimate of s t could be obtained by rapidly estimating the magnitude and direction of camera motion from MPEG motion vectors [8]. 5. EXPERIMENTAL RESULTS We applied our interpolation method to a 180 frame, singleshot, 320 x 240 test sequence captured with a digital video camera. The first and last frames (Figure 3) were designated as anchor frames, and correspondence between them was initialized using 66 user selected ....

Y.P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge. Rapid Estimation of Camera Motion from Compressed Video With Application to Video Annotation. IEEE Trans. on Circuits and Systems for Video Technology, vol. 10, no. 1, pp. 133--146, February 2000.

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Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 1, pp. 133--146, Feb. 2000.

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Y.P. Tan, D.D. Saur, S.R. Kulkarni, and P.J. Pamadge, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Trans. Circuits Syst. Video Technol., vol.10, no.1, pp.133--146, 2000.

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Y. P. Tan, D. D. Saur, S.R. Kulkami, P.J. Ramadge. "Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation". IEEE Transactions on CSVT , Vol. 10, n 1, pp. 133--146, Feb. 2000.

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Y-P. Tan, D.D. Saur, S.R. Kulkarni, and P.J. Ramadge. Rapid estimation of camera motion from compressed video with application to video annotation. IEEE Transactions on Circuits and Systems for Video Technology, 10, 2000.

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Y.P. Tan, Saur D.D., Kulkami S. R., Ramadge P.J. "Rapid estimation of camera motion from compressed video with application to video annotation", IEEE Trans. on CSVT, 10(1):133-146, 2000.

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Y. Tan, D. Saur, and S. Kulkarni, "Rapid estimation of camera motion from compressed video with application to video annotation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 1, pp. 133--146, 2000.

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Y. P. Tan, D. D. Saur, S. R. Kulkarni & P. J. Ramadge, "Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation," em IEEE Trans on Circuits and Systems for Video Technology, vol. 10, no. 1, pp. 133-46, Feb 2000.

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