• Title/Summary/Keyword: background current

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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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    • v.16 no.3
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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.

Low-light Image Enhancement Based on Frame Difference and Tone Mapping (프레임 차와 톤 매핑을 이용한 저조도 영상 향상)

  • Jeong, Yunju;Lee, Yeonghak;Shim, Jaechang;Jung, Soon Ki
    • Journal of Korea Multimedia Society
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    • v.21 no.9
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    • pp.1044-1051
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    • 2018
  • In this paper, we propose a new method to improve low light image. In order to improve the image quality of a night image with a moving object as much as the quality of a daytime image, the following tasks were performed. Firstly, we reduce the noisy of the input night image and improve the night image by the tone mapping method. Secondly, we segment the input night image into a foreground with motion and a background without motion. The motion is detected using both the difference between the current frame and the previous frame and the difference between the current frame and the night background image. The background region of the night image takes pixels from corresponding positions in the daytime image. The foreground regions of the night image take the pixels from the corresponding positions of the image which is improved by the tone mapping method. Experimental results show that the proposed method can improve the visual quality more clearly than the existing methods.

Real-Time Object Detection System Based on Background Modeling in Infrared Images (적외선영상에서 배경모델링 기반의 실시간 객체 탐지 시스템)

  • Park, Chang-Han;Lee, Jae-Ik
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.46 no.4
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    • pp.102-110
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    • 2009
  • In this paper, we propose an object detection method for real-time in infrared (IR) images and PowerPC (PPC) and H/W design based on field programmable gate array (FPGA). An open H/W architecture has the advantages, such as easy transplantation of HW and S/W, support of compatibility and scalability for specification of current and previous versions, common module design using standardized design, and convenience of management and maintenance. Proposed background modeling for an open H/W architecture design decreases size of search area to construct a sparse block template of search area in IR images. We also apply to compensate for motion compensation when image moves in previous and current frames of IR sensor. Separation method of background and objects apply to adaptive values through time analysis of pixel intensity. Method of clutter reduction to appear near separated objects applies to median filter. Methods of background modeling, object detection, median filter, labeling, merge in the design embedded system execute in PFC processor. Based on experimental results, proposed method showed real-time object detection through global motion compensation and background modeling in the proposed embedded system.

Design of the RLG Current Stabilizer for Attitude Control in the Satellite (저궤도 위성 자세제어용 RLG 전류 안정화 회로 설계)

  • Kim, Eui-Chan;Lee, Heung-Ho
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.1
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    • pp.98-101
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    • 2008
  • In this paper, we describe the RLG current stabilizer circuit for attitude control in the satellite. The RLG makes use of the Sagnac effect within a resonant of a HeNe laser. The difference between two discharge currents causes one of the gyro bias errors. The theoretical background and current stabilizer are introduced. It is verified that the circuit designed is applicable to the test of input voltage and temperature.

Motion Detection using Adaptive Background Image and Pixel Space (적응적 배경영상과 픽셀 간격을 이용한 움직임 검출)

  • 지정규;이창수;오해석
    • Journal of Information Technology Applications and Management
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    • v.10 no.3
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    • pp.45-54
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    • 2003
  • Security system with web camera remarkably has been developed at an Internet era. Using transmitted images from remote camera, the system can recognize current situation and take a proper action through web. Existing motion detection methods use simply difference image, background image techniques or block matching algorithm which establish initial block by set search area and find similar block. But these methods are difficult to detect exact motion because of useless noise. In this paper, the proposed method is updating changed background image as much as $N{\times}M$pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect motion by computing fixed distance pixel instead of operate all pixel.

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Probabilistic Background Subtraction in a Video-based Recognition System

  • Lee, Hee-Sung;Hong, Sung-Jun;Kim, Eun-Tai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.4
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    • pp.782-804
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    • 2011
  • In video-based recognition systems, stationary cameras are used to monitor an area of interest. These systems focus on a segmentation of the foreground in the video stream and the recognition of the events occurring in that area. The usual approach to discriminating the foreground from the video sequence is background subtraction. This paper presents a novel background subtraction method based on a probabilistic approach. We represent the posterior probability of the foreground based on the current image and all past images and derive an updated method. Furthermore, we present an efficient fusion method for the color and edge information in order to overcome the difficulties of existing background subtraction methods that use only color information. The suggested method is applied to synthetic data and real video streams, and its robust performance is demonstrated through experimentation.

Analysis of Induced Voltage in Superconducting Magnet System for Background magnetic Field Generation in SSTF

  • Qiuliang wang;Yoon, Cheon-Seog;Sungkeun Baang;Kim, sangbo;Park, Hyunki;Kim, Keeman
    • Proceedings of the Korea Institute of Applied Superconductivity and Cryogenics Conference
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    • 2000.02a
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    • pp.185-188
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    • 2000
  • The voltage induced in the superconducting background magnet system is analyzed according to the calculation of self inductance and mutual inductance. The voltage induced by blip and compensation coils of the background magnet system is about 6.4V. In order to charge the main background magnet, the power supply must provide the minimum voltage of 1.1 kV. the compensation coils have an influence on the field distribution. The compensation coils result in the decreasing center field about 2.67%. It can remarkably decrease the ac losses and the voltage on the current leads of the background magnet.

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On-line Background Extraction in Video Image Using Vector Median (벡터 미디언을 이용한 비디오 영상의 온라인 배경 추출)

  • Kim, Joon-Cheol;Park, Eun-Jong;Lee, Joon-Whoan
    • The KIPS Transactions:PartB
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    • v.13B no.5 s.108
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    • pp.515-524
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    • 2006
  • Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.

Design Of Intrusion Detection System Using Background Machine Learning

  • Kim, Hyung-Hoon;Cho, Jeong-Ran
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.5
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    • pp.149-156
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    • 2019
  • The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.