Vehicle classification on multi-sensor smart cameras using feature- and decision-fusion

In the proposed project we are working towards multi-sensor smart cameras, i.e., we augment vision-based cameras by additional sensors such as infrared and audio and, thus, transform a single smart camera into an embedded multi-sensor node. Our software framework for embedded online data fusion, called I-SENSE, which supports data fusion on different levels of data abstraction is presented. Further our fusion model is presented with the focus set on four main parts, namely (i) the acoustic and visual feature extraction, (ii) feature based data fusion and the feature selection algorithm, (iii) feature based decision modeling based on Support Vector Machines (SVM) and (iv) decision modeling based on a modified Dempster-Shafer approach is discussed. Finally we demonstrate the feasibility of our multilevel data fusion approach with experimental results of our 'vehicle classification" case study.