Abstract
Gait recognition is one of the most promising video-based biometric technologies. The edge of silhouettes and motion are the most informative feature and previous studies have explored them separately and achieved notable results. However, due to occlusions and variations in viewing angles, their gait recognition performance is often affected by the predefined spatial segmentation strategy. Moreover, traditional temporal pooling usually neglects distinctive temporal information in gait. To address the aforementioned issues, we propose a novel gait recognition framework, denoted as GaitASMS, which can effectively extract the adaptive structured spatial representations and naturally aggregate the multi-scale temporal information. The Adaptive Structured Representation Extraction Module (ASRE) separates the edge of silhouettes by using the adaptive edge mask and maximizes the representation in semantic latent space. Moreover, the Multi-Scale Temporal Aggregation Module (MSTA) achieves effective modeling of long-short-range temporal information by temporally aggregated structure. Furthermore, we propose a new data augmentation, denoted random mask, to enrich the sample space of long-term occlusion and enhance the generalization of the model. Extensive experiments conducted on two datasets demonstrate the competitive advantage of proposed method, especially in complex scenes, i.e. BG and CL. On the CASIA-B dataset, GaitASMS achieves the average accuracy of 93.5\% and outperforms the baseline on rank-1 accuracies by 3.4\% and 6.3\%, respectively, in BG and CL. The ablation experiments demonstrate the effectiveness of ASRE and MSTA.
Abstract (translated)
步识别是视频based生物特征技术中最具潜力的一种。轮廓的边缘和运动是最 informative 的特征,先前的研究已经单独探讨了它们并取得了显著的结果。然而,由于遮挡和视角的变化,它们的步识别性能往往受到预先定义的空间分割策略的影响。此外,传统的时间聚合通常忽视了步的特定时间信息。为了解决上述问题,我们提出了一种新的步识别框架,称为 GaitASMS,它能够有效提取适应结构的时空表示,并自然聚合多尺度的时间信息。自适应结构表示提取模块(ASRE)使用自适应边缘 mask 分离轮廓的边缘,并最大限度地扩展在语义潜在空间中的表示。此外,多尺度时间聚合模块(MSTA)通过时间聚合结构实现长短期时间信息的有效建模。我们还提出了一种新的数据增强方法,称为随机 mask,以丰富长期遮挡样本空间,并增强模型的泛化能力。在两个数据集上进行广泛的实验表明, proposed 方法的竞争优势,特别是在复杂场景下,即BG和CL。在CASIA-B数据集上,GaitASMS的平均精度为93.5%,在BG和CL的Rank-1精度方面分别比基准方法高出3.4%和6.3%。点消解实验表明ASRE和MSTA的有效性。
URL
https://arxiv.org/abs/2307.15981
