Global Coding of Multi-source Surveillance Video Data

In this paper, we exploit a new type of data redundancy in the multisource surveillance video to reduce the huge gap between the growth rate of the data and the video compression rate. Global redundancy caused by correlated appearances of moving objects in multiple videos consists of model similarity, spatial correlation and temporal consistency. Therefore, we propose a global coding scheme of moving objects to eliminate the global redundancy: a model based object reconstruction is initially employed to reconstruct the objects in the video, then a pose-based residual error prediction is developed to compensate the difference between the real video appearance and the initial reconstruction from model. The experiment with two simulated surveillance videos has proved that the proposed coding scheme can achieve better coding performance than the main profile of HEVC and surveillance profile of IEEE 1857-2013.