Multi-view Feature Fusion for Activity Classification.

In this paper, we propose and compare various approaches of feature and decision fusion for human action classification in a multi-view framework. The key difference between the employed methods is in the nature of extracted features in each view and the stage we fuse data from all cameras to classify the activity. At the feature extraction stage we utilize three different methods. At the decision making stage, the features obtained by the cameras are combined in a single classifier, or a classifier for each camera produces a local decision which is combined with decisions from other cameras for a global decision. We have employed our method on a fall detection dataset, and all the fusion approaches are compared for accuracy and complexity.