Sequential Multilinear Subspace Based Event Detection in Large Video Data Sequences

Abstract

A major portion of the big data that is produced comprises of videos coming from surveillance cameras deployed to view streets, buildings, offices etc. The surveillance videos are mainly used for monitoring day to day activities. The video sequences are long and the events of interest occur only over a short duration. Hence, there is a pressing need to analyze and detect events to avoid continuous manual monitoring of entire video sequence. The first step towards that is to extract the foreground information. In this paper we present an effective online multilinear subspace learning algorithm which incrementally learns and models the background as a low-rank tensor. This background modeling combined with appropriate post processing steps is useful to detect anomalous events, thus in turn the foreground, in the video. The efficacy of the proposed method is also brought out in the simulation results provided.