Surveillance Video Analysis using Compressive Sensing with Low Latency

We propose a method for analysis of surveillance video by using the low rank and sparse decomposition with low latency combined with compressive sensing. The proposed analysis segments the background and extracts moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to analyze the video by a low rank and sparse decomposition of a matrix. The low rank component represents the background, and the sparse component, which is obtained in a tight wavelet frame domain, is used to identify moving objects in the surveillance video. The important feature of the proposed low latency method is that the decomposition can be performed with a small number of video frames, which reduces latency in the reconstruction and makes it possible for real time processing of surveillance video. The low latency method is both justified theoretically and validated experimentally.