Head detection using HOG and SVM

1. Why SVM is used with HOG?
2. How does HOG and SVM work?
3. Why do linear SVMS trained on HOG features perform so well?
4. What is HOG face detection?

Why SVM is used with HOG?

Histogram of oriented gradients (HOG) is used for feature extraction in the human detection process, whilst linear support vector machines (SVM) are used for human classification. A set of tests is conducted to find the classifiers which optimize recall in the detection of persons in visible video sequences.

How does HOG and SVM work?

Later, HOG (Histogram of Oriented Gradients) features are extracted from large numbers of facial images to be used as part of the recognition mechanism. These HOG features are then labeled together for a face/user and a Support Vector Machine (SVM) model is trained to predict faces that are fed into the system.

Why do linear SVMS trained on HOG features perform so well?

Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks. Their popularisation can largely be attributed to the step-change in performance they brought to pedestrian detection, and their subsequent successes in deformable parts models.

What is HOG face detection?

Histogram of Oriented Gradients, also known as HOG, is a feature descriptor like the Canny Edge Detector, SIFT (Scale Invariant and Feature Transform) . It is used in computer vision and image processing for the purpose of object detection.