traffic-flow

基于Hough变换和基于Radon变换的直线检测算法代码(source code of two straight line detection algorithm (based on Hough Transform and Radon Transform))

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )

动态跟踪，边移动边绘制路线。规定经纬度的范围，调用地图的路径算法，求出路径。(Dynamic tracking, while moving to draw lines.Specified range of latitude and longitude, map the path algorithm call, find the path.)

粒子群算法的Pareto多目标函数优化，适用于多目标问题的寻优(Particle swarm optimization Pareto multi-objective function optimization, suitable for multi-objective optimization.)

说明：  可通过Hog和svm实现小狮子的分类，将其分为两类(The classification of small lions can be realized by hogs and SVM, which can be divided into two categories)

按照夹角余弦距离计算待测样品与样品库的样品相似度(Cosine distance calculation in accordance with the test sample and the sample database sample similarity)

用差分盒维数处理图像，通过对图像分块，然后每一块来计算该区域的分维数，将其作为该区域所有像素上每一像素的分维数。然后，增强分维数分布图效果，然后取阈值，最后，给人造目标加框。(Differential box counting process the image, the image block, and then every one to calculate the fractal dimension of the region, as all the pixels in the region of each pixel on the fractal dimension. Then enhanced fractal dimension distribution effect, and then take the threshold, and finally, to the man-made objects framed.)

本代码使用引导滤波器去除图像中的雨滴 在对雨雪天气图像进行清晰化处理之前，要对其中的动态成分进行自动辨识和去除。快速有效的自动辨识及去除方法是构建自适应多种低能见度条件视觉增强算法的前提条件(This code USES the guided filter to remove image in the rain Before the clarifications in rain and snow weather image processing, to the dynamic content of the automatic identification and removal.Quick and efficient automatic identification and removal method is to construct the adaptive multiple visual enhancement algorithm is the premise of low visibility conditions)

echarts-master