• The Journal of Information Technology
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• v.7 no.2
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• pp.93-104
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• 2004

Sports scene is characterized by large amount of global motion due to pan and zoom of camera motion, and includes many small objects moving independently. Some short period of sports games is thrilling to televiewers, and important to producers. At the same time that kinds of scenes exhibit exceptionally dynamic motions and it is very difficult to analyze the motions with conventional algorithms. In this thesis, several algorithms are proposed for global motion analysis on these dynamic scenes. It is shown that proposed algorithms worked well for motion compensation and panorama synthesis. When cascading the inter frame motions, accumulated errors are unavoidable. In order to minimize these errors, interpolation method of motion vectors is introduced. Affined transform or perspective projection transform is regarded as a square matrix, which can be factorized into small amount of motion vectors. To solve factorization problem, we preposed the adaptation of Newton Raphson method into vector and matrix form, which is also computationally efficient. Combining multi frame motion estimation and the corresponding interpolation in hierarchical manner enhancement algorithm of motion parameters is proposed, which is suitable for motion compensation and panorama synthesis. The proposed algorithms are suitable for special effect rendering for broadcast system, video indexing, tracking in complex scenes, and other fields requiring global motion estimation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.182-194
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• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2C
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• pp.170-183
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• 2002

This paper proposes an efficient frequency and symbol synchronization structure which could solve problems of the conventional methods to implement DVB-T receive modem which adopted OFDM transmission method. The main considerations of frequency synchronization algorithms are that the frequency tracking performance is not stable enough, and lots of symbols are required, especially when the decimal part of normalized frequency offset (which original frequency offset is divided by the subcarrier spacing) is around $\pm$0.5 to solve these problems, we propose an efficient frequency offset is divided by the subcarrier spacing) is around $\pm$0.5 by using the average of coarse synchronization over several symbol. Also, we suggest a new symbol synchronization structure which is easy to implement without performance degradation in multipath fading channel with only coarse symbol synchronization by making window offset to the range of guard interval in contrast to the conventional structure of dividing symbol synchronization in fine and coarse mode during synchronization. By extensive simulation, we have shown the superiority of the proposed schemes.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.380-392
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• 2013

A method to classify a golf swing motion into 7 sections using a Kinect sensor and a fuzzy system is proposed. The inputs to the fuzzy logic are the positions of golf club and its head, which are extracted from the information of golfer's joint position and color information obtained by a Kinect sensor. The proposed method consists of three modules: one for extracting the joint's information, another for detecting and tracking of a golf club, and the other for classifying golf swing motions. The first module extracts the hand's position among the joint information provided by a Kinect sensor. The second module detects the golf club as well as its head with the Hough line transform based on the hand's coordinate. Using a fuzzy logic as a classification engine reduces recognition errors and, consequently, improves the performance of robust classification. From the experiments of real-time video clips, the proposed method shows the reliability of classification by 85.2%.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.405-412
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• 2010

Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain 'clean' background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.

• Journal of Korean Elementary Science Education
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• v.36 no.4
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• pp.303-315
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• 2017

The purpose of this study is to analyze elementary teachers' science class objectively and quantitatively using Mobile Eye Tracker. The mobile eye tracker is easy to wear in eyeglasses form. And experiments are collected in video form, so it is very useful for realizing objective data of teacher's class situation in real time. Participants in the study were 2 elementary teachers, and they are teaching sixth grade science in Seoul. Participants took a 40-minute class wearing a mobile eye tracker. Eye movements of participants were collected at 60 Hz, and the collected eye movement data were analyzed using SMI BeGaze 3.7. In this study, the area related to the class was set as the area of interest, we analyzed the visual occupancy of teachers. In addition, we analyzed the linguistic interaction between teacher and students. The results of the study are as follows. First, we analyze the visual occupancy of meaningful areas in teaching-learning activities by class stage. Second, the analysis of eye movements when teachers interacted with students showed that teacher A had a high percentage of students' faces, while teacher B had a high visual occupation in areas not related to classes. Third, the linguistic interaction of the participants were analyzed. Analysis areas include questions, attention-focused language, elementary science teaching terminology, daily interaction, humor, and unnecessary words. This study shows that it is possible to analyze elementary science class objectively and quantitatively through analysis of visual occupancy using mobile eye tracking. In addition, it is expected that teachers' visual attention in teaching activities can be used as an index to analyze the form of language interaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.160-166
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• 2020

As mobile-based hardware technology develops, many kinds of applications are also being developed. In addition, there is an increasing demand to automatically check that the interface of these applications works correctly. In this paper, we describe a method for accurately detecting faulty images from applications by comparing major characteristics from input color images. For this purpose, our method first extracts major characteristics of the input image, then calculates the differences in the extracted major features, and decides if the test image is a normal image or a faulty image dissimilar to the reference image. Experiment results show that the suggested approach robustly determines similar and dissimilar images by comparing major characteristics from input color images. The suggested method is expected to be useful in many real application areas related to computer vision, like video indexing, object detection and tracking, image surveillance, and so on.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.25-33
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• 2010

In this paper, we propose a simple and efficient algorithm for head pose estimation using a single camera. First, four subimages are obtained from the camera image for face feature extraction. These subimages are used as feature templates. The templates are then tracked by Kalman filtering, and camera projective matrix is computed by the projective mapping between the templates and their coordinate in the 3D coordinate system. And the user's face pose is estimated from the projective mapping between the user's face and image plane. The accuracy and the robustness of our technique is verified on the experimental results of several real video sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.811-819
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• 2017

A behavior pattern analysis algorithm based on descriptors consists of information of a moving object and temporal histogram is proposed. Background learning is performed first for detecting, tracking and analyzing moving objects. Each object is identified using an association of the center of gravity of objects and tracked individually. A temporal histogram represents a motion pattern using positions of the center of gravity and time stamp of objects. The characteristic and behavior of objects are figured out by comparing each coordinates of a position history in the histogram. Behavior information which is comprised with numbers of a start and end frame, and coordinates of positions of objects is stored and managed in the linked list. Descriptors are made with the stored information and the video retrieval algorithm is designed. We confirmed the higher retrieval accuracy compare with conventional methods.