Background subtraction on depth videos with convolutional neural networks
This addresses background subtraction for computer vision applications like surveillance in scenarios where color data is unavailable, such as poor lighting, but it is incremental as it adapts CNNs to depth data.
The paper tackled background subtraction in depth videos by proposing BGSNet-D, a convolutional neural network-based approach, which outperformed existing methods on the SBM-RGBD dataset.
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.