Information fusion for obstacle recognition in visible and infrared images

We propose the information fusion of visible and infrared images for a pedestrian-vehicle SVM-based classification. Different types of fusion methods are presented: data fusion, feature fusion, matching score fusion and decision fusion. Data - level fusion assumes that the raw information is combined at the pixel level. The fusion at the feature level produces a feature vector integrating both visual and infrared information. Matching score fusion and decision fusion combine matching scores or decisions of individual obstacle recognition modules. Comparative results showed that fusion-based obstacle recognition techniques outperformed individual visual and infrared obstacle recognizers. An important advantage of these fusion-based systems is their possibility to adapt to the environmental illumination conditions due to a weighting parameter which also controls the system's final decision. Different feature extraction and feature selection algorithms have been investigated in order to retain the best suited features for the classification process.