Improving ViBe-based Background Subtraction Techniques Using RGBD Information

In this paper, we propose a framework for improving Background Subtraction techniques. This framework is based on two types of data, RGB and Depth. Our study stands for obtaining preliminary results of the background segmentation using Depth and RGB channels independently, then using an algorithm to fuse them to create the final results. The experiments on the SBM-RGBD dataset using four methods: ViBe, LOBSTER, SuBSENSE, and PAWCS proved that the proposed framework achieves an impressive performance compared to the original RGB-based techniques from the state-of-the-art.