Boosting with cross-validation based feature selection for pedestrian detection

An example-based classification algorithm to improve generalization performance for detecting objects in images is presented. The classifier integrates component-based classifiers according to the AdaBoost algorithm. A probability estimate by a kernel-SVM is used for the outputs of base learners, which are independently trained for local features. The base learners are determined by selecting the optimal local feature according to sample weights determined by the boosting algorithm with cross-validation. Our method was applied to the MIT CBCL pedestrian image database, and 54 sub-regions were extracted from each image as local features. The experimental results showed a good classification ratio for unlearned samples.