HSTR-Net: Reference Based Video Super-resolution for Aerial Surveillance with Dual Cameras

Aerial surveillance requires high spatio-temporal resolution (HSTR) video for more accurate detection and tracking of objects. This is especially true for wide-area surveillance (WAS), where the surveyed region is large and the objects of interest are small. This paper proposes a dual camera system for the generation of HSTR video using reference-based super-resolution (RefSR). One camera captures high spatial resolution low frame rate (HSLF) video while the other captures low spatial resolution high frame rate (LSHF) video simultaneously for the same scene. A novel deep learning architecture is proposed to fuse HSLF and LSHF video feeds and synthesize HSTR video frames at the output. The proposed model combines optical flow estimation and (channel-wise and spatial) attention mechanisms to capture the fine motion and intricate dependencies between frames of the two video feeds. Simulations show that the proposed model provides significant improvement over existing reference-based SR techniques in terms of PSNR and SSIM metrics. The method also exhibits sufficient frames per second (FPS) for WAS when deployed on a power-constrained drone equipped with dual cameras.