Abstract: A pedestrian detection algorithm take the non-equilibrium between positive and negative samples and the differences of classifiers into account at the same time is proposed,for which the existing algorithm that does not consider.Firstly,introduce the idea of cost-sensitive into classifier by setting cost-sensitive parameter's value,and make the classifier pay more attention to positive samples which fewer and more important.Furthermore,dynamic adjustment the kernel function parameter σwhich greater impacts on the performance of the classification algorithm,and make up a set of classifiers which mutual difference and moderately accurate.Then exclude the high similarity classifier by contrast.Finally,cascading and composing the remaining classifiers.Experiments show that compared with the classical algorithm,the proposed algorithm is conducive to improve pedestrian detection accuracy and has lower false alarm rate.