Multisource Surveillance Video Coding by Exploiting 3D and 2D Knolwedge
Yu Chen, Ruimin Hu, Jing Xiao, Zhongyuan Wang
Abstract
The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme proposed for moving objects so far has achieved high efficiency, it does not take full advantages of local information and highly relies on the accuracy of pose parameter of the objects, thus leading to large prediction residuals. In this paper, a novel surveillance video coding utilizing 3D and 2D knowledge is proposed. On the one hand, we generate a knowledge based reference frame from 3D models of the objects and incorporate it into the block based coding framework to remove global redundancy while improve the robustness to pose errors. On the other hand, 2D knowledge in the form of visual appearances of the objects in the previously encoded frames is employed to rectify the knowledge based reference frame for local redundancy removal. Experimental results demonstrate the effectiveness of our proposed method against HEVC and the knowledge based coding method.
BibTeX
@inproceedings{icassp2019_multisourcesurve,
title = {Multisource Surveillance Video Coding by Exploiting 3D and 2D Knolwedge},
author = {Yu Chen and Ruimin Hu and Jing Xiao and Zhongyuan Wang},
booktitle = {ICASSP 2019},
year = {2019}
}