Description: 一个由zafersavas于2008年完成的完全基于VC++6实现的人脸检测和人眼跟踪程序，通过设置相应的参数实现不同的功能。人脸跟踪中使用了camshift算法和Haar算法，眼睛检测中使用了自适应PCA算法和模板匹配算法，还支持文件和网络摄像头两种输入方式，经过试验，检测速度比较快和准确度也比较高。附带demo程序。 使用步骤： 菜单TrackEye Menu --> Tracker Settings 输入源Input Source: video 选择文件输入指定为： ..\Avis\Sample.avi 人脸检测算法（Face Detection Algorithm）: Haar Face Detection Algorithm 选中 “Track also Eyes” checkBox 眼睛检测算法（Eye Detection Algorithm）: Adaptive PCA 取消选择 “Variance Check” Number of Database Images: 8 Number of EigenEyes: 5 Maximum allowable distance from eyespace: 1200 Face width/eye template width ratio: 0.3 ColorSpace type to use during PCA: CV_RGB2GRAY 瞳孔检测设置（Settings for Pupil Detection） Check “Track eyes in details” and then check “Detect also eye pupils”. Click “Adjust Parameters” button: Enter “120” as the “Threshold Value” Click “Save Settings” and then click “Close” Settings for Snake Check “Indicate eye boundary using active snakes”. Click “Settings for snake” button: Select ColorSpace to use: CV_RGB2GRAY Select Simple thresholding and enter 100 as the “Threshold value” Click “Save Settings” and then click “Close” Platform: |
Size: 204022 |
Author:sichuanlu |
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Description: 该方法采用反对称双正交小波分解系数计算视频帧的方向梯度向量，再统计由梯度向量角和模值构成的联合空间二维直方图，然后计算连续帧直方图之间的距离，得到两帧之间的不连续值，最后采用自适应阈值分割，检测出镜头边界。-This method is the use of anti-symmetric biorthogonal wavelet decomposition coefficients video frame the direction of gradient vector, and then statistics from the gradient vector angle and modulus values constitute the joint space of two-dimensional histogram, and then calculating the histogram for the distance between frames, by the two frames between the consecutive values, the final adaptive threshold segmentation, shot boundary detected. Platform: |
Size: 144384 |
Author:韩瑞冬 |
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Description: 背景建模是实现运动目标检测与跟踪的关键技术之一。在实时视频监控系统中，对背景建模算法的运行时间及所提取出的背景图像的实时性有很高的要求，针对这一问题，提出了一种基于切比雪夫不等式的自适应阈值背景建模算法。算法利用切比雪夫不等式计算像素点色度变化的概率估计值，提出了一种自适应阈值分类方法，它将像素点快速分类为前景点、背景点及可疑点，再利用核密度估计方法对可疑点进行进一步分类，最后利用背景更新算法提取实时背景图像。实验结果证明，该算法能快速有效地区分特征明显的背景点与前景点，提高了背景图像提取的速度，对可疑点利用核密度估计方法降低了背景分割的误差，背景建模效果理想，运算速度快，适用于实时视频监控系统。-Background modeling is a key technology to realize the moving target detection and tracking. In real-time video surveillance system, there are high demands on uptime and background modeling algorithm is proposed to remove the background image in real time, for this problem, a Chebyshev inequality based on adaptive threshold background modeling algorithm. Cut algorithm uses to calculate the probability of Chebyshev inequality pixel color change estimates, an adaptive threshold classification method, it will be classified as pre-fast pixel of interest, background points and suspicious points, re-use kernel density estimation method suspicious point for further classification. Finally, background updating algorithm to extract real-time background image. Experimental results show that the algorithm can quickly and efficiently in the background of significant features of the region of interest with the previous point, improving the speed of extraction of the background image, the point of s Platform: |
Size: 1972224 |
Author: |
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Description: 针对户外视频监控存在光照变化这一问题, 提出一个用于准确完成目标检测的实时背景建模框架. 考虑到目标检测
的准确性要求, 建立基于帧间像素亮度差统计直方图的像素亮度扰动阈值. 在此基础上, 针对背景建模的实时性要求, 提出一种基于自回归背景模型的参数快速更新方法. 鉴于不同光照变化的适应性要求, 定义对光照变化不敏感的背景纹理模型. 上述模型统称为自回归{ 纹理(Auto regression and texture, ART) 模型, 该模型适应于户外光照变化. 基于该模型构建像素亮度和纹理置信区间用于目标检测. 实验结果表明, 该框架能适应和实时跟踪户外背景的光照变化, 并对目标进行准确检测.-Considering the appearance of illumination variation in outdoor video surveillance, a real-time background
modeling framework, which is also composed of accurate foreground detection, is established. In view of the accuracy
of foreground detection, a threshold based on the histogram of pixel0s intensity di® erence between neighboring frames is
proposed. On account of the real-time background modeling, a fast estimation approach on parameters of autoregressive
model is presented. Considering the adaptability to variable illumination, a texture background model insensitive to
outdoor illumination variation is designed. Thus, a uniform model named auto regression and texture (ART) is obtained.
According to the established con¯ dence intervals with perturbation of pixel s intensity and its local texture, foreground in
scenes with di® erent illumination variations is successfully detected. The experimental results indicate that the framework
is adaptive to and can exactly Platform: |
Size: 3717120 |
Author: |
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Description: 一个共同的视频被空间相关去噪的框架随机噪声和空间相关的固定模式噪声。首先，在每一卷的空间和时间上的相关性，利用sparsify数据在三维时空的变换域，然后3D体积的频谱系数的自适应阈值萎缩三维阵列。这样的阵列取决于特定的运动轨迹的体积，单个功率谱密度的随机和固定的模式噪声，以及噪声方差，自适应地估计在变换域。-The video was a common fixed pattern noise spatial correlation denoising framework random noise and space-related. First of all, in space and time correlation of each volume, use sparsify data in three-dimensional space transform domain adaptive threshold spectral coefficients 3D volume and shrinking three-dimensional array. Such an array depends on the particular trajectory volume, a single power spectral density of random and fixed pattern noise, and the noise variance, estimated adaptively transform domain. Platform: |
Size: 496640 |
Author:西门吹雪 |
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Search - video adaptive threshold