• KIPS Transactions on Computer and Communication Systems
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• v.9 no.6
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• pp.137-142
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• 2020

As research on security systems using IoT (Internet of Things) devices increases, the need for research to track the location of specific objects is increasing. The goal is to detect the movement of objects in real-time and to predict the radius of movement in short time. Many studies have been done to clearly recognize and detect moving objects. However, it does not require the sharing of information between cameras that recognize objects. In this paper, using the device information of the camera and the video information taken from the camera, the movement radius of the object is predicted and information is shared about the camera within the radius to provide the movement path of the object.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.32-41
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• 2008

The moving object tracking in vision based observation using video uses difference method between GMM(Gaussian Mixture Model) based background and present image. In the case of racking object using binary image made by threshold, the object is merged not by object information but by Cast-Shadow. This paper proposed the method that eliminates Cast-Shadow using backpropagation Neural Network. The neural network is trained by abstracting feature value form training image of object range in 10-movies and Cast-Shadow range. The method eliminating Cast-Shadow is based on the method distinguishing shadow from binary image, its Performance is better(16.2%, 38.2%, 28.1%, 22.3%, 44.4%) than existing Cast-Shadow elimination algorithm(SNP, SP, DNM1, DNM2, CNCC).

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.1
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• pp.40-47
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• 1998

The optical heads as the key parts of digital audio/video players are applied extensively from general household electric appliances to computer memory or game apparatuses, and the demand of them tends to be increased according to the extension of optical medium market. In this paper, the 2-axis actuator, the key component for focus servo and tracking servo of the optical head is designed as a type of the cantilever suspended by 4 wires. The design propriety is verified through its simulation and the characteristic analysis of its mock-ups. The other factors which influence the performance of 2-axis actuators besides the design factors of them, are also verified through the mock-up analysis.

• Convergence Security Journal
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• v.16 no.3_2
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• pp.25-32
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• 2016

Drone refers to an unmanned flying system according to the remote control. That is a remote control systems on the ground or a system that automatically or semi auto-piloted system without pilot on board. Drones have been used and developed before for military purposes. However there are currently utilized in a variety of areas such as logistics and distribution of relief supplies disaster areas, wireless Internet connection, TV, video shooting and disaster observation, tracking criminals etc. Especially it can be actively used in activities such as search or the structure of the disaster site, and may be able to detect the movement of people and an attacker using an infrared camera at night. Drones are very effective for private security.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.142-151
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• 2015

This paper proposes a method for detection of objects temporally stop moving in video sequences taken by a moving camera. Even though the consequence of missed detection of those objects could be catastrophic in terms of application level requirements, not much attention has been paid in conventional approaches. In the proposed method, we introduce cues for consistent detection and tracking of objects: motion potential, position potential, and color distribution similarity. Integration of the three cues in the graph-cut algorithm makes possible to detect objects that temporally stop moving and are newly appearing. Experiment results prove that the proposed method can not only detect moving objects but also track objects stop moving.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.96-99
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• 2015

본 논문에서는 중복되지 않는 서로 다른 카메라의 영상을 활용한 동일 객체 판단 및 추적 기술에 대하여 소개한다. 영상분석에서 색상 정보는 가장 기본이 되는 중요한 정보라 할 수 있다. 특히 색상 정보를 이용하는 히스토그램은 일반적으로 추적, 인식 등에 많이 사용되고 있으나 이동 객체나 조도 변화 등에 따라 성능에 차이를 보인다. 이러한 문제점을 해결하고자 본 연구에서는 동일 객체 판단을 위해 대표적으로 사용되는 히스토그램 정합의 두 알고리즘(HSV 공간에서의 Histogram matching 방법과 RGB 공간에서의MCSHR 알고리즘) 결합을 통해 분할 히스토그램은 객체를 3조각으로 나누어 전체와 각각의 히스토그램을 구하며 MCSHR을 RGB공간이 아니 Hue 공간 히스토그램으로 변경하여 유사도를 도출 하였으며 조도 변화에 강인한 모델을 만들기 위해 Controlled equalization기법을 사용하여 원 영상의 히스토그램의 확률과 평활화한 히스토그램의 확률 융합을 시도 하였다. 해당 실험의 비교 결과 기존 HSV공간에서 Histogram matching을 통한 유사도 비교보다 12.9% 향상된 정합율의 결과를 보였다. 또한 영상 정보와 스마트 기기를 통한 인식 방법의 융합을 통해 영상 내에서 동일 객체 판단에 대한 추가 정보 제공에 대해 방법론 적인 부분을 제안 하였다.

• Nutrition Research and Practice
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• v.12 no.3
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• pp.208-214
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• 2018

BACKGROUND/OBJECTIVES: The aim of this study was to evaluate the biological and sleep-promoting effects of combined ${\gamma}$-aminobutyric acid (GABA) and 5-hydroxytryptophan (5-HTP) using caffeine-induced sleepless fruit flies, ICR mice, and Sprague-Dawley rats. MATERIALS/METHODS: Video-tracking analysis was applied to investigate behavioral changes of Drosophila melanogaster. Pentobarbital-induced sleep test and electroencephalogram (EEG) patterns were used for analysis of sleep latency, duration, and quantity and quality of sleep in vertebrate models. RESULTS: Administration of combined GABA/5-HTP could significantly reverse the caffeine induced total distance of flies (P < 0.001). Also, individually administered and combined GABA/5-HTP significantly increased the total sleeping time in the caffeine-induced sleepless ICR mice (P < 0.001). In the caffeine-induced sleepless SD-rats, combined GABA/5-HTP showed significant differences in sleep quality between individual amino acid administrations (P < 0.05). CONCLUSIONS: Taken together, we identified inhibitory effects of combined GABA/5-HTP in locomotor activity, sleep quantity and quality in caffeine-induced sleepless models, indicating that combined GABA/5-HTP may be effective in patients with insomnia by providing sufficient sleep.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).