HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
TL
TL;DR
This paper proposes an end-to-end trainable deep network that performs optical flow estimation and frame reconstruction by combining inputs from both video feeds and provides significant improvement over existing video frame interpolation and RefSR techniques in terms of objective PSNR and SSIM metrics.
Abstract
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.
References20 items
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MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
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RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image
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RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
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Feature Representation Matters: End-to-End Learning for Reference-Based Image Super-Resolution
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