Description: 本文通过融合图像的颜色和梯度特征 ,实现了一种实时背景减除方法。首先融合颜色和梯度特征建立新的能量函数 然后基于图切割算法最小化能量函数 ,并对前景P 背景进行分割 最后使用光流验证前景区域的真实性 ,并更新背景模型。- Based on the fusion of color and gradient features , this paper implement s a novel approach to real-time background subtraction.Firstly , an energy function is defined based on the fusion of color and gradient features. Secondly , the graph cut s based algorithm is employed to minimize energy function and segment the foreground. Finally , average optical flow is used to make inference about the validity of foreground regions , background models are then updated.
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Author:巡洋舰 |
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Description: 学习图像处理和视频监控的入门书籍，介绍了经典的光流法来检测前景和背景内容。-Learning image processing and video surveillance of the entry-book, introduces the classical optical flow method to detect the foreground and background elements. Platform: |
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Author:yuyongfang |
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Description: 光流法之一：codebook BGS代码，参考论文Real-time foreground–background segmentation using codebook model （Kyungnam Kima,, Thanarat H. Chalidabhongseb, David Harwooda, Larry Davis），进行了简化。
实验效果：似乎噪声点太多了。-One of the optical flow method: the BGS codebook code, reference papers Real-time foreground-background a segmentation a using codebook the model (Kyungnam Kima, Thanarat of H. Chalidabhongseb David Harwooda, Larry Davis), carried out to simplify.
Experimental results: it seems that the noise point too much. Platform: |
Size: 3072 |
Author:宁静 |
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Description: 图像处理 o 梯度 边缘和角点 o 采样 差值和几何变换 o 形态学操作 o 滤波和彩色变换 o 金字塔及其应用 o 连接组件 o 图像和轮廓矩 o 特殊图像变换 o 直方图 o 匹配 结构分析 o 轮廓处理 o 计算几何 o 平面划分 运动分析和对象跟踪 o 背景统计量的累积 o 运动模板 o 对象跟踪 o 光流 o 预估器 模式识别 o 目标检测 照相机定标和三维重建 o 照相机定标 o 姿态估计 o 极线几何 函数列表 参考图像处理注意:本章描述图像处理和分析的一些函数-O gradient edge and corner of the image processing o Sampling difference and geometric transformations o filtering and color transform morphological operations o o pyramid and its application o connected components o image and contour moments o special image transformation o Histogram o matching structure analysis o contour processing o computational geometry o the plane divided motion analysis and object tracking cumulative o Motion templates o o background statistics object tracking o optical flow o predictor pattern recognition o target detection camera calibration and 3D reconstruction o camera calibration o attitude estimates o epipolar geometry function list reference image processing Note: This chapter describes the image processing and analysis functions Platform: |
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Author:diaoguangqiang |
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Description: 动态背景查分 和光流法的应用在区分车和人的识别上-The dynamic background check points and the optical flow method applied in the recognition of the distinction between cars and people Platform: |
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Author:夏杰 |
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Description: 本程序采用matlab编码，一共有包括帧间差分法、背景差分法、光流法、混合高斯模型法四种方法来实现红外目标检测的功能，代码检测易懂，适合初学者多多借鉴~-This procedure using matlab coding, a total including the inter-frame difference, background subtraction, optical flow method, Gaussian mixture model approach are four ways to achieve infrared target detection capabilities, code detection understand for beginners learn a lot ~ Platform: |
Size: 23785472 |
Author:Leo |
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Description: This demo tracks cars in a video by detecting motion using optical flow. The cars are segmented from the background by thresholding the motion vector magnitudes. Then, blob analysis is used to identify the cars. Platform: |
Size: 10296320 |
Author:doan |
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Description: 光流算法程序，用来估计背景变化的图像序列或视频序列，可以很好的用来跟踪运动目标-Optical flow algorithm is used to estimate changes in background image sequence or video sequence can be well used to track moving targets. . . Platform: |
Size: 259072 |
Author:zm |
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Description: A PIV Post-processing and data analysis toolbox-The PIVMat Toolbox for Matlab contains a set of command-line functions to import, post-process and analyse 2- and 3-components vector fields from PIV (particle image velocimetry), stereo-PIV, DIC (digital image correlation) SS (synthetic schlieren) or BOS (background-oriented schlieren) applications.
Main Features:
- Import vector fields from PIV (Particle Image Velocimetry) or other related technics, such as DIC (Digital Image Correlation), SS and BOS (Synthetic Schlieren and Background-oriented Schlieren).
- Compatibale with files from DaVis (LaVision), DPIVSoft (Meunier & Leweke), VidPIV (Oxford Laser), MatPIV and Optical Flow.
- Standard vector field operations: interpolation, filtering (median, Butterworth...), averaging (temporal, spatial, azimuthal...), derivatives computation (vorticity, divergence, strain, Q-factor...)
- Fully vectorized: all operations directly apply on arrays of fields (no for loops)
- More than 60 functions with full on-line documentation sampl Platform: |
Size: 8884224 |
Author:史明明 |
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Description: 研究了目前运动对象检测与跟踪的一些常用方法,包括时域差分法、背景差分法、基于光流场的检测方法和卡尔曼滤波、特征光流法的跟踪方法,并对各种方法进行了比较,指出其优缺点及适用范围,并给出了时域差分及背景差分方法的实验结果-Currently some of the commonly used methods to study the detection and tracking of moving objects, including difference time domain method, background subtraction method, optical flow detection and tracking method Kalman filtering, feature-based optical flow method, and various methods of comparison, pointing out its strengths and weaknesses and the scope of application, and the experimental results of differential and background difference time-domain method Platform: |
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Author:hengluo |
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Description: 基于像素的背景建模方法速度较快但不能很好地描述背景运动，光流能准确描述物体运动但计算量大，难以满足实时的要求．提出一种结合基于像素的背景建模方法速度快以及光流描述物体运动准确优点的背景建模和目标检测方法．具体来说，为静止背景建立传统基于像素的灰度背景模型，为运动背景建立光流背景模型，通过２种背景模型的有效结合快速准确地实现目标检测．实验结果表明，提出的方法建模速度与基于像素背景建模方法相当，同时，又有光流准确描述背景运动的优点，综合性能超越上述２种方法．-Faster but not a good description of the background motion background modeling method based on the speed of the pixel, the optical flow can accurately describe the movement of objects but computationally intensive, difficult to meet real-time requirements. Proposed background modeling method based on a combination of speed and a fast pixel optical flow motion of an object accurately describe the advantages of background modeling and object detection methods. Specifically, the establishment of the traditional pixel-based gray background model to establish the background model optical flow motion background, quickly and accurately achieved through the effective combination of two kinds of background model of target detection is still background. Experimental results show that the proposed method of modeling the speed of the pixel-based background modeling method is quite the same time, another advantage of accurately describe the background of the optical flow motion, the overall perform Platform: |
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Author: |
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Description: 本文选用了背景减法、帧间差分法和光流法。背景减法是通过将当前帧与背景帧相减得出灰度差值并将此差值与阈值比较判断是否有运动目标。-This paper chose the background subtraction and frame difference method and optical flow method.Background subtraction is through to the current frame and background frame subtraction gray level difference and compare the difference with threshold judgment whether there is a moving target. Platform: |
Size: 402432 |
Author:周文涛 |
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Description: 金字塔lucas-kanade光流代码，可用于动态背景，但速度较慢-Pyramid lucas-kanade optical flow codes, can be used for dynamic background, but slower Platform: |
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Author:孔祥山 |
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Description: 常见的目标检测方法主要有光流法，帧差法和背景模型法。光流法利用背景和运动目标的运动速度不同进行目标检测，计算量较大；帧差法对连续几帧图像的背景进行配准，通过前后帧的差分图像分离出运动物体；背景差法根据已知背景对图像进行差分，在运动背景下需要对背景模型进行更新。-Common target detection methods are mainly optical flow method, frame differential method and background model method. Optical flow method with different background and moving target movement speed target detection, large amount of calculation In successive frames image frame differential method for registration, the background of moving object was isolated through front and rear frame difference image Background difference method based on the background of image difference known, under the background of sports need to update the background model.
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Author:shitao |
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Description: 交通视频采集后的图像预处理。由于硬件及环境等因素的影响，摄像头提取的图像不可避免地包含噪声，图像的质量会受到不同程度的失真。因此，我们要对采集到的视频序列进行图像的预处理。
车辆检测算法。概括性的分析了基于图像处理的几种常用的车辆检测算法：帧差法、光流法，总结了各种方法的优缺点，并采用背景差法对车流量进行检测。该算法自适应能力强，计算量小，可正确判断有无车辆、完成车辆计数，实现车流量计算，为交通监管系统提供实时有效的交通参数。
车辆计数。分析常用的车辆计数方法：虚拟线圈法和目标跟踪法。并结合具体的实际应用采用基于检测带的车辆计数方式。结果表明，该方法能很好的进行车流量统计。
-Image pre-processing traffic after video capture. Due to hardware and environmental factors, the camera inevitably contain the extracted image noise, image quality will be subject to different degrees of distortion. Therefore, we want to capture the video sequence image preprocessing.
Vehicle detection algorithm. General analysis of several common vehicle detection algorithm based on image processing: frame difference, optical flow, summed up the advantages and disadvantages of each method, and using background subtraction to detect traffic flow. The algorithm is adaptive ability, a small amount of calculation can correctly determine whether the vehicle, the vehicle should count to achieve traffic flow calculation provides effective real-time traffic parameters for a traffic monitoring system.
Vehicle counting. Analysis of common vehicle counting methods: virtual coil method and target tracking method. Combined with concrete and practical applications based on vehicle detection with Platform: |
Size: 25600 |
Author:rankin |
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Description: 采用光流法形式检测背景相对稳定的运动物体，编写语言为C++，所有视觉库为opencv(The optical flow method is used to detect moving objects with relatively stable background. The language is C++, and all visual libraries are OPENCV) Platform: |
Size: 6976512 |
Author:zouwit
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