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detectingmovingtarget
- 本代码可以实时更新背景，建立背景模型，从而检测出视频窗口的目标运动物体-This code can be updated in real time background, background modeling, which aim to detect moving objects in video window
tracing-to-moving-object
- 采用了一种根据灰度特征进行模式匹配的跟踪算法提高了模板匹配法对于目标运动姿 态变化的自适应能力仿真结果表明上述算法在目标方向平移旋转以及图像背景对比度亮度发生改变时均能较好的检测到目标-It is presented in this paper that a modified version of it which incorporating with frame difference and multi-resolution matching. By introducing pre
YCbCrGauaaModel
- 利用基于YCbCr空间的高斯模型建立背景，并实时更新，通过背景差分法检测出道路中的运动车辆目标，效果较好。-Gaussian model based on the YCbCr space is used to establish the background, updating in real time. Then background subtraction method is used to detect the target of the movement of vehicles in
test
- 人体检测,在静止的背景下，检测到运动的人体目标，从而实现人体定位-body detection
bgfg_egmml
- 采用混合高斯模型对背景进行建模，然后对视频的运动目标进行检测！-By using the gaussian mixture model for background modeling, and then to detect the moving targets of video!
target-detectio
- 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.
Background-difference-method
- 背景差分法是采用图像序列中的当前帧和背景参考模型比较来检测运动物体的一种方法,其性能依赖于所使用的背景建模技术。背景差分法检测运动目标速度快，检测准确，易于实现，其关键是背景图像的获取。在实际应用中，静止背景是不易直接获得的，同时，由于背景图像的动态变化，需要通过视频序列的帧间信息来估计和恢复背景，即背景重建，所以要选择性的更新背景。-Background difference method is the use of images in the sequence of the current
Target-Detection
- 三帧差法检测运动目标位置（背景需要静止效果最好）-Three-difference frame method to detect moving target position (background still needs best)
sy5
- 改进的混合高斯背景模型，用于检测运动目标，能够有效的提取前景-Improved gaussian mixture background model, used to detect moving targets, and can effectively extract prospect
bs_movingdetecting_1
- 实现了运动目标的检测和跟踪，使用背景差分法，帧间差分法、光流法和均值漂移的方法，能够实现很好的对运动目标的跟踪-Achieve a moving target detection and tracking, using background subtraction, inter-frame difference, optical flow method and the method of mean-shift, it is possible to achieve a good track mov
MFCVideo(1)
- 先用背景差分法进行运动目标的检测，然后求出运动目标的质心，然后进行聚类。-First with the background subtraction method to detect moving targets, moving targets and then find the center of mass and clustering.
matlab_gmm
- 针对单高斯建模的不足，提出高斯混合背景模型，可以检测出比较清楚的运动目标，经实验对比，噪声较少
SAR-GMTI
- 星座SAR动目标检测，包括原始背景面目标模拟，运动点目标模拟，数据合成与配准，星座SAR动目标检测算法-Constellation SAR moving target detection, including the original background surface raw data simulation, moving point target simulation, data synthesis and registration, constellation SAR moving ta
ViBe1
- 基于vibe实现的目标跟踪，当背景不变时，能对运动的物体进行检测并跟踪。- 36/5000 Jīyú vibe shíxiàn de mùbiāo gēnzōng, dāng bèijǐng bù biàn shí, néng duì yùndòng de wùtǐ jìnxíng jiǎncè bìng gēnzōng. Based on the vibe implementation of the target tracking, when the background is c
hunhe
- 采用多个高斯分布的方式来描述背景像素点的特征，在线地更新参数和权重，实现运动检测和前景提取的同步进行，即采用混合高斯背景算法进行建模，以降低动态背景的干扰。(The characteristics of the background pixel are described by several Gaussian distributions, and the parameters and weights are updated online. Synchronization of motion d
multiObjectTracking
- 在静态背景下，提取运动物体(前景检测）并对其标号(In the static background, the moving object (foreground detection) is extracted and its label is extracted)
Edge detection
- 目标检测首先利用统计的方法得到背景模型，并实时地对背景模型进行更新以适应光线变化和场景本身的变化，用形态学方法和检测连通域面积进行后处理，消除噪声和背景扰动带来的影响，在HSV色度空间下检测阴影，得到准确的运动目标。(object detectWe use statistical methods to obtain the background model, and real-time of the background model is updated to adapt to illumina