Model of Object-Based Coding for Surveillance Video

In this paper, we explore the model of potential savingsof object-based coding for surveillance video. Moving
foreground objects in stationary camera surveillance
video are detected by a background subtraction
technique [3] and encoded with MPEG-4 object-based
coding. Experiment 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 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 the coding methods for surveillance
video.