• Title/Summary/Keyword: Background update

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Laser Spot Detection Using Robust Dictionary Construction and Update

  • Wang, Zhihua;Piao, Yongri;Jin, Minglu
    • Journal of information and communication convergence engineering
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    • v.13 no.1
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    • pp.42-49
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    • 2015
  • In laser pointer interaction systems, laser spot detection is one of the most important technologies, and most of the challenges in this area are related to the varying backgrounds, and the real-time performance of the interaction system. In this paper, we present a robust dictionary construction and update algorithm based on a sparse model of background subtraction. In order to control dynamic backgrounds, first, we determine whether there is a change in the backgrounds; if this is true, the new background can be directly added to the dictionary configurations; otherwise, we run an online cumulative average on the backgrounds to update the dictionary. The proposed dictionary construction and update algorithm for laser spot detection, is robust to the varying backgrounds and noises, and can be implemented in real time. A large number of experimental results have confirmed the superior performance of the proposed method in terms of the detection error and real-time implementation.

A Alternative Background Modeling Method for Change Detection (영상차이를 이용한 움직임 검출에 필요한 배경영상 모델링 및 갱신 기법 연구)

  • Chang, Il-Kwon;Kim, Kyoung-Jung;Kim, Eun-Tai;Park, Mig-Non
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.159-161
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    • 2004
  • Many motion object detection algorithms rely on the process of background subtraction, an important technique that is used for detecting changes from a model of the background scene. This paper propose a novel method to update the background model image of a visual surveillance system which is not stationary. In order to do this, we use a background model based on statistical qualities of monitored images and another background model that excluded motions. By comparing each changed area computed from the two background model images and current monitored image, the areas that will be updated are decided.

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An Improved Multiple Interval Pixel Sampling based Background Subtraction Algorithm (개선된 다중 구간 샘플링 배경제거 알고리즘)

  • Mahmood, Muhammad Tariq;Choi, Young Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.18 no.3
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    • pp.1-6
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    • 2019
  • Foreground/background segmentation in video sequences is often one of the first tasks in machine vision applications, making it a critical part of the system. In this paper, we present an improved sample-based technique that provides robust background image as well as segmentation mask. The conventional multiple interval sampling (MIS) algorithm have suffer from the unbalance of computation time per frame and the rapid change of confidence factor of background pixel. To balance the computation amount, a random-based pixel update scheme is proposed and a spatial and temporal smoothing technique is adopted to increase reliability of the confidence factor. The proposed method allows the sampling queue to have more dispersed data in time and space, and provides more continuous and reliable confidence factor. Experimental results revealed that our method works well to estimate stable background image and the foreground mask.

Robust Target Model Update for Mean-shift Tracking with Background Weighted Histogram

  • Jang, Yong-Hyun;Suh, Jung-Keun;Kim, Ku-Jin;Choi, Yoo-Joo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1377-1389
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    • 2016
  • This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

Smoke Detection using Block-based Difference Images and Projections (블록기반 차영상과 투영 그래프를 이용한 연기검출)

  • Kim, Dong-Keun;Kim, Won-Ho
    • The KIPS Transactions:PartB
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    • v.14B no.5
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    • pp.361-368
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    • 2007
  • In this paper, we propose a smoke detection method which is based on block-wise difference of image frames in video. Our proposed method is composed of three steps which are (a) the detection step of the changed regions against the background, (b) the background update step, and (c) the smoke determination step from the changed regions. We first construct the block mean Image of frames in video. And to extract the changed regions against the background, we use a block-wise difference between background's block mean image and a current input frame's block mean image. After applying projections in block-based difference images, we can determine the changed regions as rectangles using projections of difference images. we propose a update scheme of background's block mean image using the projections. We decide the smoke region using the femoral statistics of the central position and YUV color in the changed region.

A Study on SRU & SRU Record Update Protocol for Openness and Linkage of Resources (정보자원의 개방과 연계를 위한 SRU, SRU Record Update 프로토콜 연구)

  • Lee, Ji-Won
    • Journal of Korean Library and Information Science Society
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    • v.40 no.3
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    • pp.317-336
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    • 2009
  • Several protocols have been developed to efficiently utilize a great number of distributed resources. This paper investigated the background, operations and elements of SRU and SRU Record Update protocol, compared them with other protocols, and reviewed their implementation cases. The purpose of this pager is to broaden the understanding of the two new standards and to provide a practical guide to ensure their interoperability for libraries and information service centers which want to expose their own contents and to access to external resources.

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A Study on the Background Image Updating Algorithm for Detecting Fast Moving Objects (고속 객체 탐지를 위한 배경화면 갱신 알고리즘에 관한 연구)

  • Park, Jong-beom
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.4
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    • pp.153-160
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    • 2016
  • A developed skill of an intelligent CCTV is also advancing by using its Image Acquisition Device. The most important part in the field of detecting comparatively fast moving objects is to effectively reduce the loads on updating the background image in order to achieve real-time update. However, the ability of the current general-purpose computer extracting the texture as characteristics has limits in application mostly due to the loads on processes. In this thesis, an algorithm for real-time updating the background image in an applied area such as detecting the fast moving objects like a driving car in a video of at least 30 frames per second is suggested and the performance is analyzed by a test of extracting object region from real input image.

Implementation of a Robust Visual Surveillance Algorithm under outdoor environment (옥외 환경에강인한 영상 감시알고리듬구현)

  • Jung, Yong-Bae;Kim, Tea-Hyo
    • Journal of the Institute of Convergence Signal Processing
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    • v.10 no.2
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    • pp.112-119
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    • 2009
  • This paper describes a robust visual surveillance algorithm under outdoor environment. One of the difficult problems for outdoor is to obtain effective updating process of background images. Because background images generally contain the shadows of buildings, trees, moving clouds and other objects, they are changed by lapse of time and variation of illumination. They provide the lowering of performance for surveillance system under outdoor. In this paper, a robust algorithm for visual surveillance system under outdoor is proposed, which apply the mixture Gaussian filter and color invariant property on pixel level to update background images. In results, it was showed that the moving objects can be detected on various shadows under outdoor.

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Layered Object Detection using Adaptive Gaussian Mixture Model in the Complex and Dynamic Environment (혼잡한 환경에서 적응적 가우시안 혼합 모델을 이용한 계층적 객체 검출)

  • Lee, Jin-Hyung;Cho, Seong-Won;Kim, Jae-Min;Chung, Sun-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.3
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    • pp.387-391
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    • 2008
  • For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

A Study On Automatic Background Extraction and Updating Method (자동 배경 영상 추출 및 갱신 방법에 관한 연구)

  • 김덕래;하동문;김용득
    • Proceedings of the IEEK Conference
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    • 2003.11a
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    • pp.35-38
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    • 2003
  • In this paper, I propose an automatic background extraction method and continuous background updating technique. Because there is a movement of a vehicle and a change of a background is feeble, the area moving through the time axis is looked for and a background and a vehicle image is divided. A way to give dynamically the threshold which divides the image frame into a vehicle image and the background in a space is enforced. Through the repetition of the above-mentioned process, the background pictorial image is gained. Using the karlman filter technique, the update is done so that a background image can obey a climate situation and an environmental change in day and night. A background image processed algorithm is better than the existent one. Through simulation, the feasibility of the algorithm has been verified.

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