Choosing/Normalizing HoG parameters for object detection?

Question

I\'m using HoG features for object detection via classification.

I\'m confused about how to deal with HoG feature vectors of different lengths.

I\'ve trained

Answer 1

As you say, HOG basically uses a parameter that establishes the cell size in pixels. So if the image size changes, then the number of cells is different and then the descriptor is different in size.

The main approach is to use HOG is to use windows with the same size in pixels (the same size during training and also during testing). So extracted window should be the same size of trainingsample.

In that reference, one user says:

HOG is not scale invariant. Getting the same length feature vector for each image does not guarantee the scale invariance.

So you should use the same window size...