Detecting Video Image Changes Based on Improved Difference Map and Image Fusion

To resolve the problems of complex random noise and poor image quality in hawk-eye surveillance video images under low illumination, a surveillance video image change detection method based on K-means clustering and differential image adaptive fusion was developed. First, the log ratio operator and extreme average ratio operator were used to obtain two difference maps for two video surveillance images at different times. Then, the Laplacian pyramid was used for adaptive fusion. Next, the TVL1 dual method was used to denoise the fused image, PCA was used to fuse the denoised image and the enhanced image of the changed region, and then the adaptive median filter was used for filtering. Finally, the two-stage clustering method was used to obtain the final change detection results for the difference map. Based on our experimental findings, our method outperforms comparative algorithms in many metrics, including accuracy, robustness, time consumption, and false alarm rate.