Computer Vision News - March 2019

Every month, Computer Vision News reviews a research paper from our field. This month we have chosen Kalman Filter Based Classifier Fusion for Affective State Recognition . The authors are Michael Glodek, Stephan Reuter, Martin Schels, Klaus C. J. Dietmayer and Friedhelm Schwenker . The paper is here . Introduction Kalman Filters are a method in widespread use in the fields of object tracking and autonomous driving (navigation). Kalman Filters are highly efficient at fusion of measurements, due to their Markov chain based design (Markov assumption holds). The idea of Kalman Filters is to reduce measurement evaluation “noise” by fusing measurements from a variety of sources, even if some have missing values in certain instances, and computing what weight to give each source in each instance . The model can handle missing measurements by raising the level of uncertainty. Kalman Filter works in two stages: the prediction stage and the update stage. The prediction stage estimates a scalar -- the fusion of classifier outputs. The update stage (also known as the correction step) combines this estimate with the current measurements z. The classifier fusion method proposed in this paper can be used for any classifier type, as long as the following assumptions hold: 1) Markov assumption: future states are independent of past states. And 2) the data is sequentially structured. The figure above illustrates the model proposed in this paper: classifier fusion of a number of simple classifiers (base classifiers), with a reject option for each classifier. At every time point, the classification decision and confidence measure of every classifier are collected and fused by a Kalman filter, which then outputs a fused classification decision with a confidence measure for that decision. 4 Research - Kalman Filter Based Classifier Research by Assaf Spanier Computer Vision News An implementation of Kalman Filters for fusing the decisions of a number of classifiers