# Curvelet-enhanced transformer architecture for blurred action fine-grained detection

## TL;DR

This paper introduces a new network for recognizing human actions in videos, especially under challenging conditions like motion blur.

## Contribution

The novel Multi Curvelet Transformer Network (MCTN) uses curvelet transforms to enhance motion clarity and improve action detection.

## Key findings

- MCTN achieves a mean average precision (mAP) of 0.822 on benchmark datasets.
- The curvelet-based attention mechanisms improve spatial-temporal feature extraction.
- The network shows potential for real-time intelligent video analysis and human-computer interaction.

## Abstract

This study proposes a novel Multi Curvelet Transformer Network (MCTN) for fine-grained human behavior recognition in dynamic video scenarios. A key challenge in this field lies in accurately identifying human actions under adverse conditions such as motion blur, occlusion, and varying illumination. To address this, we introduce a motion blur restoration module leveraging the curvelet transform to enhance motion image clarity, thereby improving downstream behavior detection. Furthermore, we enhance the Transformer architecture by embedding curvelet-based multi-scale attention mechanisms, which significantly improve the model’s ability to extract spatial-temporal features at different resolutions. The proposed network also adopts a multi-curvelet transform structure to deepen semantic representation. Experimental results on benchmark datasets, including an action recognition dataset and the MSCOCO dataset, demonstrate that MCTN achieves superior performance, reaching a mean average precision (mAP) of 0.822. These results underscore the potential of MCTN in real-time intelligent video analysis and human-computer interaction applications.

The online version contains supplementary material available at 10.1038/s41598-025-33985-6.