Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

中文翻译：

---

基于动态步态特征的外观不变步态识别技术

基于步态识别的人员识别由于其独特性和对低分辨率视频的适应性而成为视觉监控的新兴趋势。现有的步态特征提取技术（例如步态轮廓和步态能量图像）依赖于人体的形状。人体的形状会根据对象的衣服和携带条件而变化。服装的选择每天都在变化，从而导致更高的组内差异和更低的组间差异。因此，步态验证和步态识别对于人的识别是必需的。此外，服装的选择受受试者的文化背景影响很大，并且公开的步态数据集缺乏用于步态识别的南亚土著服装的表示。我们提出了一种动态步态特征提取技术，该技术可通过运动估计保留时空步态模式。不同服装和携带条件下的动态步态特征适用于步态验证和识别。动态步态特征的互相关分数解决了步态验证的问题。互相关得分的标准偏差在0.12到0.2的范围内，反映出同一类别的动态步态特征之间的强相关性。我们在基于支持向量机的步态识别中达到了98.5％的准确度。此外，我们开发了一个基于多外观的步态数据集，该数据集捕获了南亚土著服饰的影响（SACV-Gait数据集）。我们评估了在CASIA-B，OUISIR-B，TUM-IITKGP和SACV-Gait数据集上的工作，并获得了98％，100％，