Real-Time Video Mining Based on SNGRLD-rLDA Model
Lin Tang, Lin Liu, Junhong Su · 2014
In this paper we introduce a novel probabilistic topic model named rLDA-SNGRLD for motions or activities mining in complex scene. Based on the improvement of rLDA model, we developed SNGRLD algorithm which can inference in real-time with massive video stream data set and mine the video latent motion topics and motion regions online. Experiments prove that the application of this model for detecting and locating abnormal events in complex scene have a good real-time performance and effectiveness.