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HOG

HOG Descriptor

Histogram of oriented gradients (HOG) are feature descriptors for the object detection purpose. Descriptor is a set of feature vectors. Each feature vector is computed from a block placed across the source image. Each element of a vector is a histogram of gradient orientations.

 

The algorithm of finding the HOG descriptor consists of the following steps:

  1. 1,Compute gradients for each pixel of an image.
  2. 2,Perform binning of gradients orientation (from 0 to 180 degrees, opposite directions count as the same).

3,Collect the histogram within a cell of pixels.

  1. 4,Weight the histogram by blocks and cells for local normalization of the contrasts.
  2. 5,Normalize the histogram.
  3. 6,Train a linear support vector machines (SVM) to detect an object. The output from the trained linear SVM is a set of coefficients for each element in a feature vector.

HOG