All-day Image Alignment for PTZ Surveillance Based on Correlated Siamese Neural Network

Image alignment is a highly researched topic in computer vision, which aligns a pair of images due to image changes. Despite the numerous studies conducted on this topic, large object transformation and huge illumination changes between a pair of images are still commonly encountered in real-world scenes, making the task of image alignment very challenging. In this paper, a novel image alignment algorithm is proposed. By inputting a pair of images that need to be aligned into the correlated siamese neural network, a series of blocks are extracted in feature layers from the reference image, and those blocks are correlated in the feature layers of the target image. Finally, the homography parameters between images are then regressed from the correlate layers. Compared with the classical image alignment algorithms, supervised deep homography, and unsupervised deep homography, the experimental results of our method demonstrate a superior performance on the image alignment tasks involving illumination changes, camera translation, and rotation.