HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
This work addresses the need for precise object tracking in surveillance by providing a method to enhance video resolution, though it appears incremental as an extension of reference-based super-resolution.
The paper tackles the problem of generating high spatio-temporal resolution video for wide area surveillance by fusing two video feeds with complementary resolutions, achieving significant improvements in PSNR and SSIM metrics over existing methods.
Wide area surveillance has many applications and tracking of objects under observation is an important task, which often needs high spatio-temporal resolution (HSTR) video for better precision. This paper presents the usage of multiple video feeds for the generation of HSTR video as an extension of reference based super resolution (RefSR). One feed captures video at high spatial resolution with low frame rate (HSLF) while the other captures low spatial resolution and high frame rate (LSHF) video simultaneously for the same scene. The main purpose is to create an HSTR video from the fusion of HSLF and LSHF videos. In this paper we propose an end-to-end trainable deep network that performs optical flow estimation and frame reconstruction by combining inputs from both video feeds. The proposed architecture provides significant improvement over existing video frame interpolation and RefSR techniques in terms of objective PSNR and SSIM metrics.