Symmetric pyramid attention convolutional neural network for moving object detection

Moving object detection (MOD) is a crucial research topic in the field of computer vision, but it faces some challenges such as shadows, illumination, and dynamic background in practical application. In the past few years, the rise of deep learning (DL) has provided fresh ideas to conquer these issues. Inspired by the existing successful deep learning framework, we design a novel pyramid attention-based architecture for MOD. On the one hand, we propose a pyramid attention module to get pivotal target information, and link different layers of knowledge through skip connections. On the other hand, the dilated convolution block (DCB) is dedicated to obtain multi-scale features, which provides sufficient semantic information and geometric details for the network. In this way, contextual resources are closely linked and get more valuable clues. It helps to obtain a precise foreground in the end. Compared with the existing conventional techniques and DL approaches on the benchmark dataset (CDnet2014), the experiments indicate that the performance of our algorithm is better than previous methods.