Associated evolution of a support vector machine-based classifier for pedestrian detection

Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work. In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.