How to prepare labeled data for action recognition video datasets using multiclass svm? [closed]

edit

thanks a lot for the explanation.....means i have to train svm for each class of videos and at the time of test i have to test input with each train svms? ..what is the criteria for assigning labels...how to assign label for each class. In opencv document it is defied as fallows

 // Set up training data
    float labels[4] = {1.0, -1.0, -1.0, -1.0};
    Mat labelsMat(4, 1, CV_32FC1, labels);

    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);

but i'm not getting in my case. i have 10 classes like running, walking , biking etc. and each class have 20 sample videos. then what should be label size and values in my case for each class.

means i have to train svm for each class

while you can do that, no, you probably should train 1 SVM with all the data you have.

.how to assign label for each class

simply like: 0:running, 1:standing, 2:whatever..

(this is also the number, the SVM will predict later.)

few more doubt please.... means in my case i have to take labels like

 float labels[10] = {0, 1, 2, 3,.......10};
 Mat labelsMat(10, 1, CV_32FC1, labels);

and training data for each class

float trainingData1[4] [n]= { feature1, feature2, feature3, feature4};
Mat trainingDataMat1(4, n, CV_32FC1, trainingData1);

then create svm for each class such as

Ptr<SVM> svm1 = SVM::create();

svm1->train(trainingDataMat1, ROW_SAMPLE, labelsMat);

here each svm should be trained with same labelsMat or i have to define different labels for each class?

and at the time testing i have to apply predict function with each svm1...svmN like

float response1 = svm1->predict(sampleMat); . . . . float responseK = svmN->predict(sampleMat);

then accuracy is calculated??