在官方示例中，Motion-Based Multiple Object Tracking和Using Kalman Filter for Object Tracking都使用了下面两个算法进行物体的识别
1、vision.ForegroundDetector
原理：The ForegroundDetector System object compares a color or grayscale video frame to a background model to determine whether individual pixels are part of the background or the foreground. It then computes a foreground mask. By using background subtraction, you can detect foreground objects in an image taken from a stationary camera.
将每一帧和一个背景帧进行对比，判断每个像素点是属于背景，还是属于罩子（罩子就是不属于背景的意思）。通过这种对比，就能识别出后面帧中运动的物体。适用范围：一个固定的摄像头所拍摄的视频。
算法：Gaussian mixture models (GMM).，呵呵，又是高斯。
使用方法见官方文档、示例。
参数，也是最重要的部分：
AdaptLearningRate，默认true，解释：
Enables the object to adapt the learning rate during the period specified by the NumTrainingFrames property. When you set this property to true, the object sets the LearningRate property to 1/(current frame number). When you set this property to false, the LearningRate property must be set at each time step.
很简单，就是设置要不要Learning。
NumTrainingFrames，默认150，解释：
用于训练背景模型的帧数，前面讲过，通过和这个背景模型对比来识别运动的物体。注意：这些数目的帧是指视频开头的部分。
LearningRate，默认0.005，解释：
Learning rate for parameter updates（说不上来，自己理解吧。）
Specify the learning rate to adapt model parameters. This property controls how quickly the model adapts to changing conditions. Set this property appropriately to ensure algorithm stability.（控制背景模型的更新速度，同时该参数影响算法的稳定性）
When you set AdaptLearningRate to true, the LearningRate property takes effect only after the training period specified by NumTrainingFrames is over.
（这个参数在初始的学习帧数，也就是上一个参数完毕后，才开始生效）
When you set the AdaptLearningRate to false, this property will not be available. This property is tunable.
MinimumBackgroundRatio，默认0.7，解释：
Threshold to determine background model
Set this property to represent the minimum of the apriori probabilities for pixels to be considered background values. Multimodal backgrounds can not be handled, if this value is too small.
一个像素被认为是属于背景的最小先验概率。个人认为这个参数控制识别的敏感性，设置比较大时，识别就比较迟钝，对细微变化忽略掉。
NumGaussians，默认5，解释：
Number of Gaussian modes in the mixture model（高斯大神的数量？大神保佑我）
Specify the number of Gaussian modes in the mixture model. Set this property to a positive integer. Typically this value is 3, 4 or 5. Set this value to 3 or greater to be able to model multiple background modes.（貌似是越大越好？然后更利于处理多背景的情况）
InitialVariance，解释：
Variance when initializing a new Gaussian mode
呵呵
一些相关的方法就不解释了。
2、vision.BlobAnalysis
原理：computes statistics for connected regions in a binary image. （将连通的区域连起来，作为识别的那个移动物体）；
输入输出参数：
Use the step syntax below with input binary image, BW, blob analysis object, H, and any optional properties. The step method computes and returns statistics of the input binary image depending on the property values specified. The order of the returned values when there are multiple outputs are in the order they are described below:
输出：区域的面积、中心点、外面的框框。
[AREA,CENTROID,BBOX] = step(H,BW) returns the area, centroid and the bounding box of the blobs when the AreaOutputPort, CentroidOutputPort and BoundingBoxOutputPort properties are set to true. These are the only properties that are set to true by default. If you set any additional properties to true, the corresponding outputs follow the AREA,CENTROID, and BBOX outputs.
输入参数有很多个，这里不一一解释，后面用到再详细说。
可设置的属性：
AreaOutputPort，CentroidOutputPort，BoundingBoxOutputPort
默认为true，表示会输出[AREA,CENTROID,BBOX]
MajorAxisLengthOutputPort，MinorAxisLengthOutputPort，OrientationOutputPort，EccentricityOutputPort，
EquivalentDiameterSquaredOutputPort，ExtentOutputPort，PerimeterOutputPort，LabelMatrixOutputPort
一些计算结果的输出，默认为false
OutputDataType，默认double，设置输出数据的格式，包括： double, single, or Fixed point
Connectivity，默认8，设置哪些像素点是连接的，可选4和8。你懂的，一个小正方形周围有8个小正方形。
MaximumCount，默认50，解释：
Maximum number of labeled regions in each input image（每幅图中最大的区域个数，也就是识别到的运动物体个数）
MinimumBlobArea，默认0，解释：
Minimum blob area in pixels（区域最小面积，占多少个像素？）
MaximumBlobArea，默认为最大的整型数intmax('uint32')
Maximum blob area in pixels（区域占的最大面积，单位像素）
以上两个属性可以控制识别到的区域大小，把不想识别到的筛选出去。
ExcludeBorderBlobs，默认false
Exclude blobs that contain at least one border pixel
Set this property to true if you do not want to label blobs that contain at least one border pixel.
排除掉有边界的区域。