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,Compute gradients for each pixel of an image.
- 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.
- 4,Weight the histogram by blocks and cells for local normalization of the contrasts.
- 5,Normalize the histogram.
- 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.