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.