Comparing 7 super-resolution methods for video surveillance
Comparing 7 super-resolution methods for video surveillance
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment
arXiv paper abstract https://arxiv.org/abs/2107.04133
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2107/2107.04133.pdf
… limitations of camera hardware, camera pose, limited bandwidth, varying illumination conditions, and occlusions, the quality of the surveillance feed is significantly degraded at times
… inspected the effectiveness of four conventional yet effective SR algorithms and three deep learning-based SR algorithms to seek the finest method that executes well in a surveillance environment with limited training data op-tions.
… six surveillance datasets has been used, consisting of individuals with varying distances from the camera, changing illumination conditions, and complex backgrounds.
… also been compared based on face detection accuracy.
… Convolutional Neural Network (CNN) based SR technique using an external dictionary proved to be best by achieving robust face detection accuracy and scoring optimal quantitative metric results under different surveillance conditions.
This is because the CNN layers progressively learn more complex features using an external dictionary.
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