Intelligent depiction of light and shadow

Addressing the issues of reduced accuracy and robustness in traditional video shadow detection methods due to occlusions and deformations caused by lighting changes and complex backgrounds, a video shadow detection method based on the Segment Anything Model (SAM) is proposed. The decoder of the SAM was fine-tuned to better suit shadow detection. Leveraging its high-precision segmentation capabilities, shadow regions were extracted, and the XMem model was introduced to integrate information from previous and subsequent frames by combining sensory, short-term,and long-term memories, thus optimizing and stabilizing the shadow detection results. Experimental results show that compared to traditional methods on the ViSha dataset, the proposed method reduced the mean absolute error by approximately 31.8% and increased the Intersection over Union (IoU) by approximately 19.7%. Both qualitative and quantitative results indicate that this method not only improves the accuracy of video shadow processing and analysis but also exhibits good robustness.