A multi-scale feature fusion spatial–channel attention model for background subtraction

Background subtraction is an essential task in computer vision, and is often used as a pre-processing step for many advanced tasks. In this work, we propose a novel multi-scale feature fusion attention mechanism network to tackle cross-scene background subtraction. The cross-fusion of feature maps at different stages of the encoder makes the features input into the decoder contain low-level and high-level information. The spatial–channel attention based on the weight matrix makes the model focus on processing information related to foreground extraction. We evaluate the proposed model on the CDnet-2014 dataset with two scene-independent evaluation strategies and obtain competitive F-Measure. In addition, to evaluate the generalization ability of the model, we perform a cross-dataset evaluation scheme on the LASIESTA and SBI2015 datasets. The overall F-Measure of the model is 0.89 and 0.93, respectively. Experimental results demonstrate that the model performs well compared to the current state-of-the-art methods.