Fast Support Vector Classifier for automated content-based search in video surveillance

In this article we present and test a specialized classifier, i.e., Fast Support Vector Classifier (FSVC), which is employed for multiple-instance human retrieval in video surveillance. Thanks to its low complexity and high performance in terms of computation and speed, FSVC is adapted to ease the generalization of the feature space using only a limited number of samples in the training process. To validate the performance, FSVC is evaluated on two standard video surveillance datasets. It obtains superior or similar results in terms of F2-Score compared to the close related state-of-the-art Support Vector Machines approaches.