Prepare multiclass posture example set and the full-sized background images set . Specify the control factors , |
the stage number , the HOG resolutions for different stages, the posture samples pass-rates for the first stages , |
and the size of train samples . Set the current stage level as , the set of stage-classifiers as . Note that, |
all sub-images cropped from full-sized background images are of size in training process. |
Train the first stage classifier as follows: |
Set , sub-images randomly cropped from images in , and . |
Train a softmax model with sample sets and HOG of specified resolution, and modify the model into two SftB |
classifiers (Eq. (7)) and (Eq. (8)) based upon the pass-rate . |
Add and to . If , go to step . Otherwise, go to step . Here represents the number of |
examples in . |
Randomly crop sub-image from an image queried from . Add to if . Repeat this process until |
reaches to . |
Reset and . And go to step . |
Train the remaining stage classifiers: |
Set example sets and as: , . |
Randomly crop from image . Add to if . Repeat this process until reaches to the |
predefined . |
Train a softmax model with sample sets and HOG of specified resolution. |
Then, modify this model into SftB classifiers and based on pass-rate , if . |
Add and to , and let . |
If , go to . Otherwise, cascade training has been finished and the procedure could be stopped. |