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Description: 视频序列变化检测是根据07年“A Martingale Framework for Detecting Concept Changes in Time Varying Data Streams”的文章改写。因为svm算法的复杂性，程序省略部分内容。-Change detection in video sequences is based on 2007
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Author: 高洪佳 |
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Description: 本程序主要用于对视频场景基于语义进行分割,先用SVM向量机进行语义分类,然后再进行分割,效果很好-This procedure is mainly used for video scene segmentation based on semantics, first vector machine SVM semantic classification, and then further divided, well
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Author: lijun |
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Description: 视频监控系统源代码，使用VC++开发，希望给正在学习VC的人带来帮助。-Source code of video monitoring System developed by VC++, hope it will be helpful to someone learning VC++.
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Author: 王海彬 |
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Description: 本文的目的是提供一个我开发的SSE优化的，C++库，用于人脸检测，你可以马上把它用于你的视频监控系统中。涉及的技术有：小波分析，尺度缩减模型（PCA,LDA,ICA），人工神经网络（ANN），支持向量机（SVM），SSE编程，图像处理，直方图均衡，图像滤波，C++编程，还有一下其它的人脸检测的背景知识-The purpose of this paper is to provide an I developed SSE optimized, C++ library, used for face detection, you can immediately use it for your video surveillance system. The technology involved are: wavelet analysis, scaling down model (PCA, LDA, ICA), artificial neural network (ANN), support vector machine (SVM), SSE programming, image processing, histogram equalization, image filtering, C++ programming, but also what other people have the background knowledge of face detection
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Author: 憨豆 |
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Description: jsvm参考软件 SVM可伸缩编码参考软件-jsvm SVM scalable video coding reference software Reference Software
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Size: 2154496 |
Author: Tim |
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Description: libsvm使用中的参数说明和视频教程以及程序中数据的格式要求-libsvm-use instructions and video tutorials, and parameter data in the format of the program requirements
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Author: jzxcjzxc |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类， 提出了一种基于聚类的核主成分分析梯度方向直方图和二叉决策树支持向量机的运动目标分类算法．利用背景减法提取运动目标前景区域， 并识别出潜在候选运动目标．利
用提出的基于聚类的核主成分分析的梯度直方图描述子提取候选运动目标的特征， 以较低维数的数据有效地描述运动目标的有效特征． 将提取的运动目标特征输入二叉决策树支持向量机， 实现多类目标的准确分类． 通过在不同视频序列上的实验验证， 提出的算法对运动目标进行较好地分类， 而且在运算速度方面较传统目标分类方法有了明显的提高． 实验结果证明了算法对运动目标分类具有较好的准确性 可靠性和鲁棒性．-For the purpose of semantically classifying moving objects accurately in a surveillance scene，a moving objects classification method based on the clustered kernel principal component analysis ( CKPCA) of the histogram
of oriented gradients ( HOG) and support vector machine ( SVM) was proposed． Firstly，the moving areas in the
foreground were extracted by means of the background subtraction method，and some of them were identified as potential candidates of moving objects． Secondly，the characteristics of the moving objects were obtained by the CKPCA- HOG descriptor，which could describe the moving objects＇ effective features at a lower data dimension． Finally，the data characteristics were fed into a binary SVM decision tree，and the final multi- class classification results were obtained accurately． After verifying different video sequences，the algorithm was able to classify moving targets very well． Compared with traditional classification methods，the proposed method makes obvious improv
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Author: 高峰 |
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Description: 一个实例搞定libsvm分类《Learn SVM Step by Step》by faruto2011系列视频-应用篇-An instance to get the the libsvm classification " Learn the SVM Step by Step " by faruto2011 Series Video- Beginner s Guide
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Author: kuangbo |
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Description: 本文的目的是提供一个我开发的SSE优化的，C++库，用于人脸检测，你可以马上把它用于你的视频监控系统中。涉及的技术有：小波分析，尺度缩减模型（PCA,LDA,ICA），人工神经网络（ANN），支持向量机（SVM），SSE编程，图像处理，直方图均衡，图像滤波，C++编程，还有一下其它的人脸检测的背景知识。-Face expression identification code, part of the realization of the algorithm and write the purpose of this paper is to provide a I development of SSE optimized, c++ library for face detection, you can get it for your video monitoring system. Regarding technical: wavelet analysis, dimension reduction model (PCA, LDA, ICA), artificial neural network (ANN), support vector machine (SVM), SSE programming, image processing, histogram equalization, image filtering, c++ programming, and the other face detection background knowledge.
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Author: 青哥 |
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Description: 为正在做视频跟踪的人们提供一些数据。在进行动作识别时，需要利用SVM分类器，然后将人的动作分为几类，然后进行训练。这是一些跑姿势的数据。-Provide some data for the people who are doing the video track. During action recognition, need to use the SVM classifier, then action will be divided into several categories of people, and then training. This is some of the posture data run.
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Author: 水木清华 |
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Description: This folder contains the following sub-folders which are essential in our project:
1.Raw Data
All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St.
2.Filter
Filter to rule out signal of Channel 1 and Channel 6
3.SVM_quadratic kernel_39
SVM code with quadratic kernel
4.PCA+KNN_62 :
PCA+KNN code
5.PCA+SVM_polynomial Kernel_50
PCA+SVM code with polynomial kernel
6.ANN
Artificial Neural Network code
7.PlotTerrain
Project demo video with actual result -This folder contains the following sub-folders which are essential in our project:
1.Raw Data
All the raw data collected from Flagstaff hill, CMU Athletic Field, and Railroad on Neville St.
2.Filter
Filter to rule out signal of Channel 1 and Channel 6
3.SVM_quadratic kernel_39
SVM code with quadratic kernel
4.PCA+KNN_62 :
PCA+KNN code
5.PCA+SVM_polynomial Kernel_50
PCA+SVM code with polynomial kernel
6.ANN
Artificial Neural Network code
7.PlotTerrain
Project demo video with actual result
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Author: Chao |
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Description: 利用SVM机器学习对视频中行人进行识别。本工程的识别率较高。如有不足之处，请大家多多指点。-Using SVM machine learning of pedestrian to identify in the video. The project has higher recognition rate. If there are any shortcomings, please advice for me.
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Author: 屠震元 |
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Description: 视频图像的人群密度检测，多种人群密度场景下人群计数算法：
算法功能:建立图像特征和图像人数的数学关系
算法输入:训练样本图像1,2…K
算法输出:模型估计参数 ，参考图像
算法流程：1)对训练样本图像进行分块处理(算法1.1)；
2)通过算法1.2，计算训练样本各个对应分块的ALBP特征归一化，再用K-means算法(可使用opencv等算法库实现，不再描述其算法)，将图像块分成k(k<K)类，获取k(k<K)个聚类中心，即为参考图像；
3)对分块的图像进行与参考图像进行匹配。使用算法1.2求取ALBP特征，并求取其相似度 ，将相似性集合作为新特征并形成一个归一化的新特征 。
4)按照行人面积占图像块面积的比例，以60 为分界，分布采用径向基核函数 和线性核函数 。K(xi,x)建立图像特征和图像人数的SVR(支持向量回归机)模型可使用opencv中的SVM或libsvm，输出模型估计参数 。
-Population density detection of video images, a variety of crowd density scenes crowd counting algorithm:
Algorithm functions: a mathematical relation between the image features and the number of images
Algorithm Input: training sample image 1,2 ... K
Algorithm output: model estimation parameters, reference image
Algorithmic process: 1) the training sample image into blocks (algorithm 1.1)
2) by 1.2 algorithm to calculate the corresponding training samples of each block ALBP features normalized, then K-means algorithm (algorithm can be used opencv library implementation, no longer describe the algorithm), the image block is divided into k (k <K) class, gets k (k <K) clustering centers, namely the reference image
3) conduct of image block matching with the reference image. 1.2 ALBP characterized using an algorithm to strike, and strike the similarity, the similarity of a set of new features and forming a normalized new features.
4) pedestrian area accounted for in
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Author: 徐云华 |
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Description: The sign language recognition to create a to store the training image, the video capture infront the user image
and each sign language assign the letter the preprocessing the image then image is convert into rgg to cbcr. The image is segmented
feature extract the shape and skin color then use the svm classification.
-The sign language recognition to create a to store the training image, the video capture infront the user image
and each sign language assign the letter the preprocessing the image then image is convert into rgg to cbcr. The image is segmented
feature extract the shape and skin color then use the svm classification.
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Author: thenu |
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Description: Libsvm-FarutoUltimate版本介绍与使用（《Learn SVM Step by Step》by faruto2011系列视频-应用篇）-Libsvm-FarutoUltimate version Introduction and use (" Learn SVM Step by Step" by faruto2011 Series Video- Application)
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Author: |
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Description: 一个自己封装的SVM类，可以实现对正负样品图片的剪切，分类器的训练，利用原来的文件测试分类器，利用一段视频测试分类器，同时也可以完成分类器的引用，同时SVM的训练参数可调。-Reference to a category of their own package of SVM, can achieve the image on the positive and negative samples of shear, training a classifier, using the original file test the classifier, using a video test classifier. At the same time, it can complete the classifier and SVM training parameters adjustable.
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Author: 黄志举 |
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Description: vad_directed_by_noise_classification.m
This code is an implementation of VAD algorithm proposed in:
Robust voice activity detection directed by noise classification
please cite the article in your paper:
Robust voice activity detection directed by noise classification, J Saeedi, SM Ahadi, K Faez
Signal, Image and Video Processing, 1-12
the folder is also contained the following
* different noise models for svm
* different sub_functions.
* three speech signal TIMIT dataset and their vad labels
Note that you need to download noise dataset from
http://spib.rice.edu/spib/select.
and libsvm toolbox from
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
It should be mentioned that both speech and noise should be sampled at 8 KHz.
Jamal Saeedi
Amirkabir University of Technology
Electrical Engineering Department-vad_directed_by_noise_classification.m
This code is an implementation of VAD algorithm proposed in:
Robust voice activity detection directed by noise classification
please cite the article in your paper:
Robust voice activity detection directed by noise classification, J Saeedi, SM Ahadi, K Faez
Signal, Image and Video Processing, 1-12
the folder is also contained the following
* different noise models for svm
* different sub_functions.
* three speech signal TIMIT dataset and their vad labels
Note that you need to download noise dataset from
http://spib.rice.edu/spib/select.
and libsvm toolbox from
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
It should be mentioned that both speech and noise should be sampled at 8 KHz.
Jamal Saeedi
Amirkabir University of Technology
Electrical Engineering Department
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Author: Ilya |
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Description: This paper advances prior work by proposing a joint learning
framework to simultaneously identify the spatial and temporal
extents of the action of interest in training videos. To get pixel-level
localization results, our method uses dense trajectories extracted
videos as local features to represent actions. We first
present a trajectory split-and-merge algorithm to segment a video
into the background and several separated foreground moving
objects. In this algorithm, the inherent temporal smoothness
of human actions is exploited to facilitate segmentation. Then,
with the latent SVM framework on segmentation results, spatial
and temporal extents of the action of interest are treated as
latent variables that are inferred simultaneously with action
recognition. Experiments on two challenging datasets show that
action detection with our learned spatial and temporal extents is
superior than state-of-the-art methods.
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Author: robin |
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Description: 大学数值分析算法，实现了图像的灰度化并进一步用于视频监视控，包括最小二乘法、SVM、神经网络、1_k近邻法。- University of numerical analysis algorithms, Achieve a grayscale image and further control for video surveillance, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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Author: foujiu |
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Description: 基于python+opencv 的 人脸识别，对一段视频进行读取，并检测出人脸，然后进行PCA 降维，最后用SVM进行人脸识别，识别率94%左右。(Based on python + opencv face recognition, a video was read, and face detection, and then PCA dimension reduction, and finally SVM face recognition, recognition rate of about 94%.)
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Author: qinjian09 |
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