Multiple Action Detection in Videos

Abstract

In this work, we present the efficient detection of multiple actions occurring simultaneously in streaming video of various real-world applications using a frame differencing-based method for background detection. We compare our method with other modeling methods (such as multi-channel nonlinear SVM) for multiple action detection on various video datasets. We demonstrate through quantitative performance evaluation metrics such as performance accuracy, standard deviation and detection F-score, and the efficacy of the proposed method over those reported in the literature.