Selective integration of local‐feature detector by boosting for pedestrian detection

AbstractAn example‐based classification algorithm for pedestrian detection 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 subregions were extracted from each image as local features. The experimental results showed a good classification ratio for unlearned samples. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 177(4): 12–22, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21195