How to train my own waldboost detector to detect vehicles in OpenCV 3.3

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Recently I have paid many time in finding an efficient method to detect car objects using OpenCV. I have found that in opencv 3.0, one of the update (in the release note, https://github.com/opencv/opencv/wiki... ) says:

"Car detection using Waldboost, ACF by Vlad Shakhuro and Nikita Manovich "

And in Opencv 3.1, the update becomes:

"Improved ICF detector, waldboost implementation – opencv_contrib/xobjdetect (Vlad Shakhuro, Alexander Bovyrin)"

I have also found that some one had successfully applied the waldboost detector to detect cars, such as the following two papers:

1. C. Caraffi, T. Vojíř, J. Trefný, J. Šochman and J. Matas, "A system for real-time detection and tracking of vehicles from a single car-mounted camera," 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, 2012, pp. 975-982.
2. J. Sochman and J. Matas, "WaldBoost - learning for time constrained sequential detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 150-156 vol. 2.

These achievements indicate that Waldboost detector should be valid / suitable for car detection, which is well fit my purpose.

To accomplish this goal, I have collect enough positive samples/negative samples. I use opencv 3.3 with opencv 3.3 contribution modules for experiment. After correct configuration, there should be an executable file named "opencv_waldboost_detector", which can be used for training my own classifier.

I used the following command to train my classifier (samples in folder "positiveImgs" are scaled to 24x24 according to the code, if I fully understand)(however, I do not apply any scale operations to the images in "negativeImgs", only keeping them unchanged):

./opencv_waldboost_detector train waldboost.xml ./positiveImgs ./negativeImgs

After finishing my training, the output of the terminal ( I work under ubuntu 16.04) is somewhat like （only an example, because I have lost the original data while writing this question):

i=1009 feat= 6439 thr=195 casthr=0.586 alpha=0.095 err=0.453 loss=1.044e-2 neg 361/353 i=1010 feat= 6423 thr=240 casthr=0.684 alpha=0.098 err=0.451 loss=1.089e-2 neg 353/345 i=1011 feat= 1312 thr= 97 casthr=0.578 alpha=0.106 err=0.447 loss=1.050e-2 neg 345/345

And also I got a *.xml file which is the target classifier. However, when I test this classifier, the detect objects are very strange. It can not detect any valid car, but only some background rectangle. I want to know what is wrong with this trained classier. Or are there any important steps which I ignored? (I do not know how to upload the screenshots)

End state of training from terminal:

training examples (negative images, around 50000 examples ):

training examples (positive images, about 10000+ examples):

detect results (using the trained xml file ):

Could someone tells me what is wrong with the waldboost detector? why it does not work for my problem?

Thank you for anyone who answers this question in advance.