Haarcascading XML does not work properly - c++

Question

I'm pretty new to OpenCV and cascade training. I did train a cascade to recognize "running" motion. I used Haar-like and LBP. For haar-like I used 1181 positive images and 3866 negative images and 12 stages. For LBP I used 2426 positive images and 1031 negative images and 12 staged.
Following, you could see a screenshot of each one.
After a while waiting for training to be finished (around 3-4 hours for each one) I would be able to create the xml files from AdaBoost chart classifiers.
The problem is that when I use them on my code to see how they are working, they don't recognize any of obvious running motions! The code is written with C++ and OpenCV 3.3.
Does anyone have any idea why my cascade xml files after training do not working properly?
C++ code is as below:
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/imgproc.hpp>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
//open from file
VideoCapture video("../data/videos/people_activity/run/run2.mp4");
Mat img;
Mat frmg;
// Load cascate classifier placed in sulution folder
CascadeClassifier bodydetector, rundetector;
string bodycascadeName = "haarcascade_fullbody.xml";
string runcascadeName = "lbpcascade_run.xml";
bool bodyloaded = bodydetector.load(bodycascadeName);
bool runloaded = rundetector.load(runcascadeName);
// Parameters of detectMultiscale Cascade Classifier
int groundThreshold = 1;
double scaleStep = 1.1;
Size minimalObjectSize(40, 40);
Size maximalObjectSize(200, 200);
// Vector of returned bodies and run
std::vector<Rect> bodyfound, runmotion;
while (video.read(img))
{
// Image from video read and store to Mat
video >> img;
if (img.rows == 0)//end of video
break;
// Convert input to greyscale
Mat image_grey;
cvtColor(img, image_grey, CV_BGR2GRAY);
bodyfound.clear();
// Detect bodies
bodydetector.detectMultiScale(image_grey, bodyfound, scaleStep, groundThreshold, 0 | 2, minimalObjectSize, maximalObjectSize);
for (size_t k = 0; k < bodyfound.size(); k++)
{
//draw a rectangle with red color around it
rectangle(img, bodyfound[k].br(), bodyfound[k].tl(), Scalar(0, 0, 0), 1, 8, 0);
Mat bodyROI = img(bodyfound[k]);
//Detect running for bodies if there is any!
rundetector.detectMultiScale(bodyROI, runmotion, scaleStep, groundThreshold, 0 | 2, minimalObjectSize, maximalObjectSize);
//if any were found!
if (runmotion.size() > 0) {
for (size_t i = 0; i < runmotion.size(); i++)
{
//draw a rectangle with red color around it
rectangle(img, runmotion[i].br(), runmotion[i].tl(), Scalar(80, 0, 255), 1, 8, 0);
}
}
}
imshow("result", img);
int c = waitKey(10);
if ((char)c == 27) { break; } // escape
}
return 0;
}