Compressive sensing based video object compression schemes for surveillance systems

Abstract

In some surveillance videos, successive frames exhibit correlation in the sense that only a small portion changes (object motion). If the foreground moving objects are segmented from the background they can be coded independently requiring far fewer bits compared to frame-based coding. Huang et al proposed a Compressive Sensing (CS) based Video Object Error Coding (CS-VOEC) where the objects are segmented and coded via motion estimation and compensation. Since motion estimation might be computationally intensive, encoder can be kept simple by performing motion estimation the decoder rather than at the encoder. We propose a novel CS based Video Object Compression (CS-VOC) technique having a simple encoder in which the sensing mechanism is applied directly on the segmented moving objects using a CS matrix. At the decoder, the object motion is first estimated so that a CS reconstruction algorithm can efficiently recover the sparse motion-compensated video object error. In addition to simple encoding, simulation results show our coding scheme performs on par with the state-of-the-art CS based video object error coding scheme. If the object segmentation requires more computations, we propose to deploy a distributed CS framework called Distributed Compressive Video Sensing based Video Object Compression (DCVS-VOC) wherein the object segmentation is done only for key frames.