• 제목/요약/키워드: Movement Detection

검색결과 602건 처리시간 0.028초

Study on mobile agents for the intrusion detection in pervasive computing environment (퍼베이시브 컴퓨팅 환경에서의 침입탐지용 모바일 에이전트에 대한 연구)

  • Oh, Byung-Jin;Um, Nam-Kyoung;Mun, Hyung-Jin;Lee, Sang-Ho
    • Journal of the Korea Society of Computer and Information
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    • 제11권3호
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    • pp.231-237
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    • 2006
  • Pervasive computing environment is similar to the meaning of ubiquitous computing, however it is a kind of the commercial product, which is made from the collaboration between NIST and IBM. On the basis of this environment, the research of mobile agents for intrusion detection is going on in progress. In this paper, we study the research about mobile agents for the intrusion detection and then suggest scenarios using moving mobile agents based on the multiple mobile agents in the intrusion detection. Subsequently, we could figure out the problems which occurred through progress of integrity movement as a matter of the intrusion detection.

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MPEG Video Segmentation Using Frame Feature Comparison (프레임 특징 비교를 이용한 압축비디오 분할)

  • 김영호;강대성
    • Journal of the Institute of Convergence Signal Processing
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    • 제4권2호
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    • pp.25-30
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    • 2003
  • Recently, development of digital technology is occupying a large part of multimedia information like character, voice, image, video, etc. Research about video indexing and retrieval progresses especially in research relative to video. In this paper, we propose new algorithm(Frame Feature Comparison) for MPEG video segmentation. Shot, Scene Change detection is basic and important works that segment it in MPEG video sequence. Generally, the segmentation algorithm that uses much has defect that occurs an error detection according to a flash of camera, movement of camera and fast movement of an object, because of comparing former frames with present frames. Therefore, we distinguish a scene change one more time using a scene change point detected in the conventional algorithm through comparing its mean value with abutted frames. In the result, we could detect more corrective scene change than the conventional algorithm.

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The Evaluations of Fish Survival Rate and Fish Movements using the Tagging Monitoring Approach of Passive Integrated Transponders (PIT) (수동형 전자발신장치(Passive Integrated Transponder, PIT) 모니터링 기법 적용에 따른 어종별 생존율 평가 및 어도에서 어류이동성 평가)

  • Choi, Ji-Woong;An, Kwang-Guk
    • Journal of Environmental Science International
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    • 제23권8호
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    • pp.1495-1505
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    • 2014
  • The objective of this study was to evaluate survival rate and fish movement (migration) using a tagging approach of passive integrated transponder (PIT) in Juksan Weir, which was constructed as a four major river restoration projects. For this study, survival rates of each fish species and the mobility of fish individuals were analyzed during 2 weeks by the insertion of PIT tags to various fish species in the laboratory. According to tagging tests in the laboratory, the survival rate 37.5% (30 survivals of 80 individuals) after the insertion of PIT tags. The survival rate of Carassius auratus and Hemibarbus labeo was 100% and 80% after the insertion of the tags, respectively, whereas it was only 13.3% for Zacco platypus. In the field experiments of Juksan Weir, 6 species and 157 individuals from 8 species (563 individuals) were detected in the fixed automatic data-logging system, indicating a detection rate of 27.9% in the fishway of Juksan Weir. In the meantime, some species with no or low detection rates in the fixed automatic data-logging system were turn out to be stagnant-type species, which prefer stagnant or standing water to live.

Sector Based Scanning and Adaptive Active Tracking of Multiple Objects

  • Cho, Shung-Han;Nam, Yun-Young;Hong, Sang-Jin;Cho, We-Duke
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제5권6호
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    • pp.1166-1191
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    • 2011
  • This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

Real-Time Detection of Moving Objects from Shaking Camera Based on the Multiple Background Model and Temporal Median Background Model (다중 배경모델과 순시적 중앙값 배경모델을 이용한 불안정 상태 카메라로부터의 실시간 이동물체 검출)

  • Kim, Tae-Ho;Jo, Kang-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • 제16권3호
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    • pp.269-276
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    • 2010
  • In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

Movement Detection Algorithm Using Virtual Skeleton Model (가상 모델을 이용한 움직임 추출 알고리즘)

  • Joo, Young-Hoon;Kim, Se-Jin
    • Journal of the Korean Institute of Intelligent Systems
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    • 제18권6호
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    • pp.731-736
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    • 2008
  • In this paper, we propose the movement detection algorithm by using virtual skeleton model. To do this, first, we eliminate error values by using conventioanl method based on RGB color model and eliminate unnecessary values by using the HSI color model. Second, we construct the virtual skeleton model with skeleton information of 10 peoples. After matching this virtual model to original image, we extract the real head silhouette by using the proposed circle searching method. Third, we extract the object by using the mean-shift algorithm and this head information. Finally, we validate the applicability of the proposed method through the various experiments in a complex environments.

A Stay Detection Algorithm Using GPS Trajectory and Points of Interest Data

  • Eunchong Koh;Changhoon Lyu;Goya Choi;Kye-Dong Jung;Soonchul Kwon;Chigon Hwang
    • International Journal of Internet, Broadcasting and Communication
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    • 제15권3호
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    • pp.176-184
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    • 2023
  • Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

System implementation for Qshing attack detection (큐싱(Qshing) 공격 탐지를 위한 시스템 구현)

  • Hyun Chang Shin;Ju Hyung Lee;Jong Min Kim
    • Convergence Security Journal
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    • 제23권1호
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    • pp.55-61
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    • 2023
  • QR Code is a two-dimensional code in the form of a matrix that contains data in a square-shaped black-and-white grid pattern, and has recently been used in various fields. In particular, in order to prevent the spread of COVID-19, the usage increased rapidly by identifying the movement path in the form of a QR code that anyone can easily and conveniently use. As such, Qshing attacks and damages using QR codes are increasing in proportion to the usage of QR codes. Therefore, in this paper, a system was implemented to block movement to harmful sites and installation of malicious codes when scanning QR codes.

Assessment of the Posture Function by Head Movement (상체움직임에 따른 자세기능의 평가)

  • Kim, Jeong-Lae;Hwang, Kyu-Sung;Nam, Youg-Seok
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • 제14권5호
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    • pp.131-135
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    • 2014
  • The purpose of this study was to show the detection of the head movement within relatively the posture function. An analysis of the posture function was inquired a displacement that the ranges of stance direction showed generally a variation across all condition through the head movement. CNS condition (C_RL-MIN-AVG) was verified slightly greater variation at $0.226{\pm}0.04$ units. Somatosensory condition (So_$RL-_{MIN-AVG}$) was verified slightly greater variation at $0.939{\pm}0.46$ units. Vestibular condition (Ve_$RL-_{MIN-AVG}$) was verified slightly greater variation at $4.009{\pm}1.05$ units. Vision condition (Vi_$RL-_{MIN-AVG}$) was verified greater variation at $8.336{\pm}4.05$ units. When the movement head of vision characteristic function was presented a diminutive variance. On the CNS characteristic condition of the movement head function was presented a diminutive variance.

Flexible Background-Texture Analysis for Coronary Artery Extraction Based on Digital Subtraction Angiography (유동적인 배경 텍스쳐 분석을 통한 DSA 기반의 관상동맥 검출)

  • Park Sung-Ho;Lee Joong-Jae;Lee Geun-Soo;Kim Gye-Young
    • The KIPS Transactions:PartB
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    • 제12B권5호
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    • pp.543-552
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    • 2005
  • This paper proposes the extraction of coronary arteries based on DSA(Digital Subtraction Angiography) through a texture analysis of background in the angiography. DSA is a well established modality for the visualization of coronary arteries. DSA involves the subtraction of a mask image - an image of the heart before injection of contrast medium - from live image. However, this technique is sensitive to the movement of background and can result to a wrong detection by the variance of background gray-level intensity between two images. Therefore, this paper solves a structural problem resulted from a background movement bV selecting an image which has the least difference of movement through an analysis of the similarity of background texture and proposes a method to extract only the blood vessel efficiently through local gray-level correction of the selected image. Using the coronary angiogram of 5 patients clinical data, we proved that the proposed method has the lower false-detection rate, approximately $2\%$, and the higher accuracy than the existing methods.