针对步态识别模型在特征表示粒度和时空依赖建模的不足,提出了一种融合多尺度特征表示和注意力机制的步态识别模型.该模型包含两个关键模块:多尺度特征融合网络(multi-scale features fusion network,MFFN)和步态注意力融合模块(gait attention fusion module,GAFM).其中,MFFN通过多尺度、多粒度特征融合提高特征表示的丰富性和判别力;GAFM通过自适应地关注步态序列中的关键帧和重要区域,从而有效地建模长期时空依赖关系.在 3个数据集CASIA-B,CASIA-B*和OUMVLP上的实验结果表明,该模型在多种复杂条件下均优于现有模型,相较于基准模型,平均识别率分别提升了 0.9%,0.3%和 0.6%.
A gait recognition model fusing multi-scale feature representation and attention mechanism
To address limitations in gait recognition model regarding feature representation granularity and spatio-temporal dependency modeling,a novel model fusing multi-scale feature representation and attention mechanisms was proposed.The model consists of two key modules:multi-scale features fusion network(MFFN)structure and gait attention fusion module(GAFM).MFFN enhances the richness and discriminative power of feature representation through multi-scale and multi-granular feature fusion.GAFM effectively modeled long-term spatio-temporal dependencies by adaptively focusing on key frames and important regions in gait sequences.Experimental results on CASIA-B,CASIA-B*,and OUMVLP datasets show that the model outperforms existing models under various complex conditions,with average recognition rate improved by 0.9%,0.3%and 0.6%respectively compared to the baseline model.
万方数据
维普
