AdaBoost Training Algorithm for Viola-Jones Object Detection

In the Viola-Jones object detection algorithm, the training process uses AdaBoost to select a subset of features and construct the classifier. A large set of images, with size corresponding to the size of the detection window, is prepared. This set must contain positive examples for the desired filter (e.g. only front view of faces), and negative examples (nonfaces). Each image has index l, l = 1…L. For each image, a corresponding value y_l, is established. y_l=1 for faces and y_l=0 for nonfaces.

Initialize weights  , for y_l=0,1 respectively where P_– and P₊ are the number of nonfaces and faces in the image set.

The algorithm is executed for an arbitrary number of rounds, I.

For i = 1…I

1. Normalize the weights as follows so that w_i,l is a probability distribution:

2. For each feature j, train a classifier h_j which is restricted to using a single feature. The classifier’s error rate is evaluated with respect to w_i,l:

3. Choose the classifier, h_i, with lowest error ε_i.

Update the weights:

The final classifier is:

More Information

• Video Surveillance
• Face Detection using Viola-Jones Algorithm
• Video Codecs
• Video Design
• Video Reference Designs