  Background subtraction is a significant component of computer vision systems.
It is widely used in video surveillance, object tracking, anomaly detection,
etc. A new data source for background subtraction appeared as the emergence of
low-cost depth sensors like Microsof t Kinect, Asus Xtion PRO, etc. In this
paper, we propose a background subtraction approach on depth videos, which is
based on convolutional neural networks (CNNs), called BGSNet-D (BackGround
Subtraction neural Networks for Depth videos). The method can be used in color
unavailable scenarios like poor lighting situations, and can also be applied to
combine with existing RGB background subtraction methods. A preprocessing
strategy is designed to reduce the influences incurred by noise from depth
sensors. The experimental results on the SBM-RGBD dataset show that the
proposed method outperforms existing methods on depth data.
