New gait recognition framework fuses body shape and locomotion dynamics

Researchers have developed a new gait recognition framework using deep residual networks and multi-branch feature fusion to improve accuracy in surveillance and security applications. The system employs HRNet for skeletal keypoint estimation and a ResNet-50 backbone to extract features from body proportion, gait velocity, and skeletal motion. A novel Multi-Branch Feature Fusion module, inspired by channel-wise attention, dynamically weights these features. Experiments on the CASIA-B benchmark showed a Rank-1 accuracy of 94.52% under normal walking conditions, outperforming existing skeleton-based methods for coat-wearing scenarios. AI

影响 Enhances biometric security capabilities with improved accuracy in challenging conditions.

排序理由 This is a research paper detailing a new method for gait recognition.

在 arXiv cs.CV 阅读 →

• arXiv
• ResNet-50

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →