Getting error using SVM with SURF [closed]

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Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst;

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst;

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video