Filtering SVM frame-by-frame binary classification in a detection framework

Classifying frames, or parts of them, is a common way of carrying out detection tasks in computer vision. However, frame by frame classification suffers from sudden significant variations in image texture, colour and luminosity, resulting in noise in the extracted features and consequently in the decisions taken. Support Vector Machines have been widely validated as powerful tools for frame by frame detection of non-separable datasets, but are extremely sensitive to these variations between adjacent frames, creating as consequence sudden flickering in the classification results. This work proposes a Dynamic Bayesian Network to smooth the classification results of Support Vector Machines (SVM) in detection tasks. The method is evaluated in First Person Vision (FPV) videos, where a SVM is used to decide whether or not the user´s hands are in his field of view.