• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1058-1065
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• 2006

In this paper, we propose a new motion vector coding algorithm suitable for MPEG-4 Part 10 video coding standard. In the proposed algorithm, the amount of the motion for a given video sequence is Predicted by using a characteristic of the motion vector distribution for the neighboring blocks as well as the MB_type, which is the syntax element in the standard. And one of the independent coding and the combined coding methods is adaptively employed to compress the motion vector difference. Simulation results show that the proposed algorithm outperforms the conventional methods without additional memory and calculations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.20-28
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• 2003

Recently we need a faster and more accurate motion vector search algorithm for ASIC(Application Specific IC) or small systems. Block motion estimation using Full Search(FS) algorithm provides the best visual quality and PSNR, but it requires intensive computations. The previously proposed fast algorithms reduced the number of computations by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher SAD(Sum of Absolute Difference) for motion compensated images. In this paper we exploit the spatial correlation of motion vectors and present a fast motion estimation scheme which uses the predicted motion vector(PMV). The PMV scheme is more clear and simpler than the previously proposed algorithms which also use adjacent motion vectors. Simulation results with standard video sequences show that the PMV scheme is faster and more accurate than other algorithms such as Nearest-Neighbors Search(NNS) algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.250-257
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• 2011

In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.62-76
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• 1994

In this paper, a segmentation-based coding technique as applied to video sequences is proposed. A proposed method separates an image into contour and texture parts, then the visually-sensitive contour part is represented by chain codes and the visually-insensitive texture part is reconstructed by a representative motion vector of a region and mean of the segmented frame difference. It uses a change detector to find moving areas and adopts variable blocks to represent different motions correctly. For better quality of reconstructed images, the displaced frame difference between the original image and the motion compensated image reconstructed by the representative motion vector is segmented. Computer simulation with several video sequences shows that the proposed method gives better performance than the conventional ones in terms of the peak signal to noise ratio(PSNR) and compression ration.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.179-187
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• 2009

In this paper, an efficient multi-level successive elimination algorithm using the locality in block was proposed for motion estimation. If SAD(sum of absolute difference) is calculated from large absolute difference values to small absolute difference values, SAD is increased rapidly. So, partial distortion elimination in SAD calculation can be done very early. Hence, the computations of SAD calculation can be reduced. In this paper, an efficient algorithm to calculate SAD from large absolute difference values to small absolute difference values by using the locality in block. Experimental results show that the proposed algorithm is an efficient algorithm with 100% motion estimation accuracy for the motion estimation of motion vectors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4587-4605
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• 2019

High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.7
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• pp.807-818
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• 1988

This paper proposes new algorithms for the estimation of displacement vector and moving object extraction using difference picture. First, the relations between the boundary of moving objects in two consecutive image and the boundary of difference picture regions are analyzed, then displacement vector estimation algorithm is proposed. Using the estimated displacement vector, moving objects are directly extracted from difference picture. Since the proposed algorithms do not process gray-valued image, they have a short processing time and are suitable to real time processing. From the experimental results, we observed that, if difference picture is wel extracted, the proposecd algorithms work well even in the circumstances of complex background, fast or slow motion, rotation etc., including occlusion where is not moving area.

• Geophysics and Geophysical Exploration
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• v.5 no.2
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• pp.99-107
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• 2002

An analytical forward modeling algorithm was developed for the efficient application to the geotechnical engineering in SASW (Spectral Analysis of Surface Waves) method. for the theoretical dispersion curve, the finite difference method using motion stress vector, which was proposed by Aki and Richards, was employed and verified with two earth models. For the stable and fast calculation, it was found that the model size depending on the frequency range is suitable $1.5\~2$ times bigger than the wavelength.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.273-281
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• 2004

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the prediction search algorithm for block matching using the temporal correlation of the video sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 1.06㏈ as depend on the video sequences and improved about 0.19∼0.46㏈ on an average except the full search(FS) algorithm.