• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.408-412
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• 2009

In this paper, a new efficient algorithm for global motion estimation is proposed. This algorithm uses a previous 4-parameter model based global motion estimation algorithm and M-estimator for improving the accuracy and robustness of the estimate. The first algorithm uses the block based motion vector fields and which generates a coarse global motion parameters. And second algorithm is M-estimator technique for getting precise global motion parameters. This technique does not increase the computational complexity significantly, while providing good results in terms of estimation accuracy. In this work, an initial estimation for the global motion parameters is obtained using simple 4-parameter global motion estimation approach. The parameters are then refined using M-estimator technique. This combined algorithm shows significant reduction in mean compensation error and shows performance improvement over simple 4-parameter global motion estimation approach.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.2
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• pp.35-44
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• 2002

The translation and the rotation of camera occur global motion which affects all over the frame in video sequence. With the video sequences containing global motion, it is practically impossible to extract exact video objects and to calculate genuine object motions. Therefore, high compression ratio cannot be achieved due to the large motion vectors. This problem can be solved when the global motion compensated frames are used. The existing camera motion estimation methods for global motion compensation have a large amount of computations in common. In this paper, we propose a simple global motion estimation algorithm that consists of linear equations without any repetition. The algorithm uses information .of symmetric points in the frame of the video sequence. The discriminant conditions to distinguish regions belonging to distant view from foreground in the frame are presented. Only for the distant view satisfying the discriminant conditions, the linear equations for the panning, tilting, and zooming parameters are applied. From the experimental results using the MPEG test sequences, we can confirm that the proposed algorithm estimates correct global motion parameters. Moreover the real-time capability of the proposed technique can be applicable to many MPEG-4 and MPEG-7 related areas.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.913-924
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• 1991

In this paper, we propose a motion estimation algorithm using hierarchical structure. The algorithm uses the image pyramids from the repetitive application of Gaussian filtering and decimation, and performs an inter-level displacement propagation in its motion estimation process. The motion estimation algorithm based on the hierarchical structure is shown to be very effective since this scheme utilizes the local imformation as well as the global imformation. The experimental results on the various data imdicate that compared to the Horn and Schunck's method, the proposed algorithm yields an accurate motion estimation with a fast convergence behaviour.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1053-1059
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• 2010

This paper treats the DIS (Digital Image Stabilization) problem subject to base motions such as translation, rotation and zoom. For the local motion estimation from a raw image, the Harris corner detection algorithm is exploited to extract feature points, and comparing those of consecutive images, the zoom ratio (scale factor) is computed. For the global motion estimation, an equivalent model is derived to account for a 3-dimensional composite motion from which the center point and Euler angle can be determined. Finally, the motion compensation follows. To show the effectiveness of the present DIS scheme, experimental results for synthetic images are illustrated.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.205-211
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• 2014

This paper reports an efficient algorithm for accurate rolling shutter distortion removal. A hierarchical global motion estimation approach for a group of blocks reduces the level of computation by three orders of magnitude. In addition, the motion of each scanline is determined accurately by averaging two candidates obtained through cubic spline interpolation. The experimental results show that the proposed method produces accurate motion information with significant computation reduction and corrects the rolling shutter distortion effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.694-701
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• 2004

In this paper, we proposed a feature-based tracking system that traces moving objects with a pan-tilt camera after separating the global motion of an active camera and the local motion of moving objects. The tracking system traces only the local motion of the comer features in the foreground objects by finding the block motions between two consecutive frames using a block-based motion estimation and eliminating the global motion from the block motions. For the robust estimation of the camera motion using only the background motion, we suggest a dominant motion extraction to classify the background motions from the block motions. We also propose an efficient clustering algorithm based on the attributes of motion trajectories of corner features to remove the motions of noise objects from the separated local motion. The proposed tracking system has demonstrated good performance for several test video sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.594-600
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• 2017

This paper proposes a global search based motion estimation algorithm for high performance HEVC encoder and its hardware architecture. To eliminate temporal redundancy, motion estimation in HEVC inter-view prediction uses global search and fast search algorithm to search for a predicted block having a high correlation with the current PU in an interpolated reference picture. The global search method predicts the motion of all candidate blocks in a given search area, thus ensuring optimal results, but has a disadvantage of large computation time. Therefore we propose a new algorithm that reduces computational complexity by reusing SAD operation in global search to reduce computation time of inter prediction. As a result of applying the proposed algorithm to standard software HM16.12, the computation time was reduced by 61%, BDBitrate by 11.81%, and BDPSNR by about 0.5% compared with the existing search algorithm. As a result of hardware design, the maximum operating frequency is 255 MHz and the total number of gates is 65.1K.

• The Journal of the Korea Contents Association
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• v.3 no.1
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• pp.9-13
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• 2003

We propose a block motion estimation algorithm based on a statistical image feature for video sequences. The statistical feature of the reference block is obtained, then applied to select the candidate starting points (SPs) in the regular starting points pattern (SPP) by comparing the statistical feature of reference block with that of blocks which are spread ower regular SPP. The final SPs are obtained by their Mean Absolute Difference(MAD) value among the candidate SPs. Finally, one of conventional fast search algorithms, such as BRGDS, DS, and three-step search (TSS), has been applied to generate the motion vector of reference block using the final SPs as its starting points. The experimental results showed that the starting points from fine SPs were as dose as to the global minimum as we expected.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.