Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
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DOI：
10.3390/app131910975
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发表时间：
2023-10
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影响因子：
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通讯作者：
Hongyi Ye;Tanfeng Sun;Ke Xu
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文献类型：
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作者：
Hongyi Ye;Tanfeng Sun;Ke Xu
步态是一种可以从远处识别的生物行为特征，如今越来越受到人们的关注。许多现有的基于轮廓的方法忽略了步态的瞬时运动，这是区分具有相似形状的人的重要因素。为了进一步强调人类步态中的瞬时运动因素，提出了步态光流图像（GOFI），将瞬时运动方向和强度添加到原始步态轮廓中。 GOFI 还有助于利用时间和空间条件噪声。然后，通过步态光流网络（GOFN）提取步态特征，该网络包含一个集合转换（ST）架构，用于将图像级特征聚合为集合级特征，以及一个固有特征金字塔（IFP），用于利用多尺度部分特征。组合损失函数用于评估不同步态之间的相似性。在两个广泛使用的步态数据集 CASIA-B 和 CASIA-C 上进行了实验。实验表明GOFN在两个数据集上都有更好的表现，这说明了GOFN的有效性。
Gait is a kind of biological behavioral characteristic which can be recognized from a distance and has gained an increased interest nowadays. Many existing silhouette-based methods ignore the instantaneous motion of gait, which is an important factor in distinguishing people with similar shapes. To further emphasize the instantaneous motion factor in human gait, the Gait Optical Flow Image (GOFI) is proposed to add the instantaneous motion direction and intensity to original gait silhouettes. The GOFI also helps to leverage both the temporal and spatial condition noises. Then, the gait features are extracted by the Gait Optical Flow Network (GOFN), which contains a Set Transition (ST) architecture to aggregate the image-level features to the set-level features and an Inherent Feature Pyramid (IFP) to exploit the multi-scaled partial features. The combined loss function is used to evaluate the similarity between different gaits. Experiments are conducted on two widely used gait datasets, the CASIA-B and the CASIA-C. The experiments show that the GOFN performs better on both datasets, which shows the effectiveness of the GOFN.