Model of Object-Based Coding for Surveillance Video
Yang Yu, David Doermann · 2006
In this paper, we explore the model of potential savings of object-based coding for surveillance video. Moving foreground objects in stationary camera surveillance video are detected by a background subtraction technique and encoded with MPEG-4 object-based coding. Experimental results show that compared with frame-based coding, object-based coding can achieve significant savings which are dependent on the video content. We further model the relationship of compression efficiency and the number and size of video objects using a statistical learning method. Simulations show that the model is representative. The model can be used to predict the savings of object-based coding and select coding methods for surveillance video.