• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1143-1151
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• 2019

In this paper, we propose a wireless transmission scheme that maximizes transmission efficiency per frequency bandwidth in a single carrier modulation scheme. The proposed scheme employs a decision directed channel tracking technique to remove both pilot signal and the guard interval signal between symbols in frames. It performs a raised cosine pulse shaping function with an roll-off factor of 0.05. In addition, 2×2 multiple input and multiple output using two polarized antennas is applied and both equalization and signal separation are performed in the frequency domain. The wireless modem with this technology confirms that the transmission speed of up to 63.3Mbps is achieved under the 5MHz frequency bandwidth

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.7
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• pp.53-60
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• 2009

Motion estimation for H.264/AVC video CODEC is very complex and requires a huge amount of computational efforts because it uses multiple reference frames and variable block sizes. We propose the architecture of high-performance integer-pixel motion estimation circuit based on fast algorithms for multiple reference frame selection, block matching, block mode decision and motion vector estimation. We also propose the architecture of high-performance interpolation circuit for sub-pixel motion estimation. We described the RTL circuit in Verilog HDL and synthesized the gate-level circuit using 130nm standard cell library. The integer-pixel motion estimation circuit consists of 77,600 logic gates and four $32\times8\times32$-bit dual-port SRAM's. It has tile maximum operating frequency of 161MHz and can process up to 51 D1 (720$\times$480) color in go frames per second. The fractional motion estimation circuit consists of 22,478 logic gates. It has the maximum operating frequency of 200MHz and can process up to 69 1080HD (1,920$\times$1,088) color image frames per second.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.377-385
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• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.514-522
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• 2003

Contrary to the previous methods, in this paper, we propose an enhanced shot change detection method using multiple features and binary decision tree. The previous methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using multiple features, which are supplementary each other, rather than using single feature. In order to classify the shot changes, we use binary classification tree. According to this classification result, we extract important features among the multiple features and obtain threshold value for each feature. We also perform the cross-validation and droop-case to verify the performance of our method. From an experimental result, it was revealed that the EI of our method performed average of 2% better than that of the conventional shot change detection methods.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.114-125
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• 2002

In this paper, we are proposing a hierarchical segmentation method that first segments the video data into units of shots by detecting cut and dissolve, and then decides types of camera operations or object movements in each shot. In our previous work[1], each picture group is divided into one of the three detailed categories, Shot(in case of scene change), Move(in case of camera operation or object movement) and Static(in case of almost no change between images), by analysing DC(Direct Current) component of I(Intra) frame. In this process, we have designed two-stage hierarchical neural network with inputs of various multiple features combined. Then, the system detects the accurate shot position, types of camera operations or object movements by searching P(Predicted), B(Bi-directional) frames of the current picture group selectively and hierarchically. Also, the statistical distributions of macro block types in P or B frames are used for the accurate detection of cut position, and another neural network with inputs of macro block types and motion vectors method can reduce the processing time by using only DC coefficients of I frames without decoding and by searching P, B frames selectively and hierarchically. The proposed method classified the picture groups in the accuracy of 93.9-100.0% and the cuts in the accuracy of 96.1-100.0% with three different together is used to detect dissolve, types of camera operations and object movements. The proposed types of video data. Also, it classified the types of camera movements or object movements in the accuracy of 90.13% and 89.28% with two different types of video data.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.680-691
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• 2021

Due to the recent developments of attaining 3D contents by using devices such as 3D scanners, the diversity of the contents being used in AR(Augmented Reality)/VR(Virutal Reality) fields is significantly increasing. There are several ways to represent 3D data, and using point clouds is one of them. A point cloud is a cluster of points, having the advantage of being able to attain actual 3D data with high precision. However, in order to express 3D contents, much more data is required compared to that of 2D images. The size of data needed to represent dynamic 3D point cloud objects that consists of multiple frames is especially big, and that is why an efficient compression technology for this kind of data must be developed. In this paper, a motion estimation and compensation method for dynamic point cloud objects using 3D DCT is proposed. This will lead to switching the 3D video frames into I frames and P frames, which ensures higher compression ratio. Then, we confirm the compression efficiency of the proposed technology by comparing it with the anchor technology, an Intra-frame based compression method, and 2D-DCT based V-PCC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.247-254
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• 2008

H.264 provides a good coding efficiency compared with existing video coding standards, H.263, MPEG-4, based on the use of multiple reference frame for variable block size motion estimation, quarter-pixel motion estimation and compensation, $4{\times}4$ integer DCT, rate-distortion optimization, and etc. However, many modules used to increase its performance also require H.264 to have increased complexity so that fast algorithms are to be implemented as practical approach. In this paper, among many approaches, fast mode decision algorithm by skipping variable block size motion estimation and spatial-predictive coding, which occupies most encoder complexity, is proposed. This approach takes advantages of temporal and spatial properties of fast mode selection techniques. Experimental results demonstrate that the proposed approach can save encoding time up to 65% compared with the H.264 standard while maintaining the visual perspectives.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• Journal of KIISE
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• v.44 no.12
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• pp.1319-1332
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• 2017

In recent years, the number of video cameras that are used to record and broadcast live sporting events has increased, and selecting the shots with the best view from multiple cameras has been an actively researched topic. Existing approaches have assumed that the background in video is fixed. However, this paper proposes a best view selection method for cases in which the background is not fixed. In our study, an athlete of interest was recorded in video during motion with multiple cameras. Then, each frame from all cameras is analyzed for establishing rules to select the best view. The frames were selected using our system and are compared with what human viewers have indicated as being the most desirable. For the evaluation, we asked each of 20 non-specialists to pick the best and worst views. The set of the best views that were selected the most coincided with 54.5% of the frame selection using our proposed method. On the other hand, the set of views most selected as worst through human selection coincided with 9% of best view shots selected using our method, demonstrating the efficacy of our proposed method.