Vision-Based Pedestrian Detection -- Improvement and Verification of Feature Extraction Methods and SVM-Based Classification

Feature extraction and classification are two of the most important modules of any vision-based pedestrian detection system, since they are critical to the performance of the system as a whole. This paper presents the feature extraction and classification modules of a vision-based pedestrian detection system using a vehicle-mounted monochrome camera. The feature extraction module includes two kinds of features: wavelet-based features and a combination of simple symmetry and edge density features. Support vector machines based on a modified version of libSVM (Chang and Lin, 2001) are used for classification, and, for feature selection and optimization of feature space size, a fast and simple method using image masks for both feature types is presented. We have trained and tested our system using pedestrian and non-pedestrian images extracted from video sequences showing daylight urban traffic scenes