HOG is a visual descriptor i.e., it describes the content of an image in a single feature vector. The idea behind HOG is that local object appearance and shape within an image can be described by the distribution of intensity gradients or edge directions.
|Training Set||Test Set||Total|
Each sample is of dimension 64x128
we train a soft-margin linear SVM classifier. We use svmlight to train the SVM model.
Accuracy on train and test set
|Accuracy (%)||Precision/Recall (%)|