  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.
