Obstacle recognition using multiple kernel in visible and infrared images

We propose a fusion model at data-level based on a linear combination of kernels for an SVM-based classification. The kernel functions are evaluated on disjoint entries, on the signature acquired from the visible and infrared spectrum. Different feature extraction and feature selection algorithms have been investigated in order to compute different feature vectors. A bi-objective optimization (using accuracy rate and classification time) is used to assure the kernel selection, the hyperparameters optimization but also the adaptation of the system to different difficult conditions using the sensor weighting coefficient. Our purpose is to develop the obstacle recognition module and to obtain a robust model for an SVM-multiple-kernel based classification.