Abstract
It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in gait-based application methods because they significantly change a person's appearance. This paper proposes a novel method for classifying human gender in real time using gait information. The use of an average gait image (AGI), rather than a gait energy image (GEI), allows this method to be computationally efficient and robust against view changes. A viewpoint (VP) model is created for automatically determining the viewing angle during the testing phase. A distance signal (DS) model is constructed to remove any areas with an attachment (carried items, worn coats) from a silhouette to reduce the interference in the resulting classification. Finally, the human gender is classified using multiple view-dependent classifiers trained using a support vector machine. Experiment results confirm that the proposed method achieves a high accuracy of 98.8% on the CASIA Dataset B and outperforms the recent state-of-the-art methods.
Abstract (translated)
在实际应用中，人们通常会随意走动、拿东西或穿厚大衣。这些因素是基于步态的应用方法的挑战，因为它们显著地改变了一个人的外表。本文提出了一种利用步态信息对人体性别进行实时分类的新方法。使用平均步态图像（agi），而不是步态能量图像（gei），可以使该方法计算效率高，并对视图变化具有鲁棒性。创建了一个视点（VP）模型，用于在测试阶段自动确定视角。距离信号（DS）模型的建立是为了从轮廓中去除任何带有附件（携带物品、磨损的外套）的区域，以减少由此产生的分类中的干扰。最后，利用支持向量机训练的多视点相关分类器对人类性别进行分类。实验结果表明，该方法在CASIA数据集B上达到了98.8%的高精度，优于目前最先进的方法。
URL
https://arxiv.org/abs/1905.01013
