Low-resolution activity recognition using super-resolution and model ensemble networks

In real-world video super-resolution, the complexity and diversity of degradations pose substantial challenges during both training and inference. Videos captured in real-world settings often depict activities at varying resolutions. Typically, these activities are filmed from a distance that reduces the resolution of imagery, which thus lacks discriminative features. To address this problem, we introduce an activity recognition solution. First, a unique integration of data transformation and attention-based average discriminator are employed for super-resolution feature augmentation. This approach mitigates the lack of discriminative cues in low-resolution videos. Subsequently, high-resolution features extracted from the recovered data are directly fed into a model ensemble for activity recognition. We evaluate the resulting method on the TinyVIRAT-v2 and HMDB51 datasets, achieving improved visual quality by leveraging the super-resolution and model ensemble strategy. The proposed method enhances the quality of textures and boosts activity recognition in low-resolution videos.