Considering that shadows caused by changes in lighting are difficult to identify and segment for intelligent surveillance videos in indoor environments, this study proposes a UNet network combining the transfer learning method and the SENet channel attention mechanism. Specifically, because shadow features are blurry and difficult to extract effectively, the SENet channel attention mechanism is added to the upsampling part of the UNet model to improve the feature weight of the effective area without increasing the network parameters. A pre-trained VGG16 network is then migrated into the UNet model to achieve feature migration and parameter sharing, improve the generalization ability of the model, and reduce training costs. Finally, the segmentation result is obtained by a decoder. The experimental results show that compared with the original UNet algorithm, the improved UNet algorithm offers significantly enhanced performance indicators, with its segmentation accuracy on moving objects and shadows respectively reaching 96.09% and 92.24% and a mean intersection-over-union (MIOU) of 92.58%.