• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.100-103
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• 2010

In this paper we propose a method of detecting moving object in H.264/AVC bitstream by representing the $4{\times}4$ block partition units as nodes of graph. By constructing hierarchical graph by taking into account the relation between nodes and the spatial-temporal relations between graphs in frames, we are able to track small objects, distinguish two occluded objects, and identify objects that move and stop alternatively.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.65-71
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• 2013

In this work, new smart phone based moving target detection is proposed. In order to implement the task, methods of real time image transmission from network camera, motion detecting algorithm and its effective implementation are also addressed. The network camera transfers image data by MJPEG format which contains various information such as data and IP address, and the smart phone separates the image data received through a WiFi module. Later, the image data is converted to a Bitmap image format, and with the help of the embedded OpenCV library on a smart phone and algorithm, it was found that the moving object was identified effectively in terms of real time monitoring and detection.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.673-676
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• 2003

This paper deals with the problem of moving object detection and location in computer vision. We describe a new object-dependent motion analysis method for tracking target in an image sequence taken from a moving platform. We tackle these tasks with three steps. First, we make an active contour model of a target in order to build some of low-energy points, which are called kernels. Then we detect interest points in two windows called tracking windows around a kernel respectively. At the third step, we decide the correspondence of those detected interest points between tracking windows by the probabilistic relaxation method In this algorithm, the detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.56-62
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• 2004

This research attempts to suggest a detecting method for cutting position of an object using the neural network, which is one of intellectual methods, and the digital image processing method. The extraction method of object information using the image data obtained from the CCD camera as a replacement of traditional analog sensor thanks to the development of digital image processing. Accordingly, this research determines the threshold value in binary-coding of an input image with the help of image processing method and the neural network for the real-time gray-leveled input image in substitution for lighting; as a result, a specific position is detected from the processed binary-coded image and an actual system designed is suggested as an example.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1313-1318
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• 2015

Current touch pen requires the special equipments to detect a touch and its price increases in proportion to the screen size. In this paper, we propose a method for detecting a touch and implementing a pen using the depth information. The proposed method obtains a background depth image using a depth camera and extracts an object by comparing a captured depth image with the background depth image. Also, we determine a touch if the depth value of the object is the same as the background and then provide the pen event. Using this method, we can implement a cheaper and more convenient touch pen.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.297-302
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• 2009

In this paper, we propose advanced screen detector as a tool for selecting the object for tracking and estimating its distance from a screen using fuzzy logic in vision based pointing device. Our system classifies the line component of the input image into horizontal and vertical lines and applies the fuzzy rule to obtain the best line pair which constitute peripheral framework of the screen. The proposed system improves the detection ratio for detecting the screen in relative to the detector used in the previous works for hand-held type vision based pointing device. Also it allows to detect the screen even though a small part of it may be hidden behind other object.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.205-214
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• 2020

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.15-25
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• 2014

In this paper, we propose a loitering detection using trajectory probability distribution and local direction descriptor for intelligent surveillance system. We use a background modeling method for detecting moving object and extract the motion features from each moving object for making feature vectors. After that, we detect the loitering behavior person using K-Nearest Neighbor classifier. We test the proposed method in real world environment and it can achieve real time and robust detection results.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1039-1040
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• 2008

Multi-resolution approach to object detection wherein all entities including the partial information and background knowledge are modeled in set-theoretic terms whereby associated processing are formulated via set-theoretic operations is investigated. The generic set-theoretic paradigm is then applied to particular problems of detecting malfunctions in semiconductor fabrication process wherein the computational- and storage- efficiencies as enabled by morphological signal processing further coupled with flexibilities enabled by multi-resolution approach leads to a scalable paradigm in which the desired performance can be obtained on-demand fashion.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.6-96
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• 2002

In this study an image processing system is designed and developed, which can detect and assort some defects on the surface of an object moving with high speed. For real time surface detection of high speed moving object, the fast processing should be managed and the image information including some surface features should be captured. It is difficult to acquire the noise free image due to various light sources and high speed moving materials under the environment of the general industrial site. In general, because pre-processing methods are employed for getting a noise free feature, the image processing speed has some limitation and the expensive image processing devices are required. This...