Gait Recognition via Gait Period Set

Runsheng Wang;Yuxuan Shi;Hefei Ling;Zongyi Li;Ping Li;Boyuan Liu;Hanqing Zheng;Qian Wang
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引用次数: 2

Abstract

Gait recognition has promising application prospects in surveillance applications, with the recently proposed video-based gait recognition methods affording huge progress. However, due to the poor image quality of some gait frames, the original frame-level features extracted from gait silhouettes are not discriminative enough to be aggregated as gait features utilized during the final recognition. Besides, as a type of periodic biometric behavior, periodic gait information is considered efficacious for capturing typical human walking patterns and refining frame-level gait features. Therefore, this paper proposes a novel approach that exploits periodic gait information, named Gait Period Set (GPS), which divides the gait period into several phases and ensembles the gait phase features to refine frame-level features. Then, features from different phases are aggregated into a video-level feature. Moreover, the refined frame-level features are aggregated as the refined gait phase features with higher quality, which can be used to re-refine the frame-level features. Hence, we upgrade the GPS into the Iterative Gait Period Set (IGPS) to iteratively refine the frame-level features. The results of extensive experiments on prevailing gait recognition datasets validate the effectiveness of the GPS and IGPS modules and demonstrate that the proposed method achieves state-of-the-art performance.
通过步态周期设置进行步态识别
步态识别在监控应用中具有广阔的应用前景,近年来提出的基于视频的步态识别方法取得了巨大的进展。然而,由于一些步态帧的图像质量较差,从步态轮廓中提取的原始帧级特征没有足够的判别能力,无法聚合为最终识别中使用的步态特征。此外,作为一种周期性的生物特征行为,周期性步态信息被认为是捕获典型人类行走模式和精炼帧级步态特征的有效方法。因此,本文提出了一种利用周期步态信息的新方法——步态周期集(GPS),该方法将步态周期划分为多个阶段,并对步态阶段特征进行集成,以细化帧级特征。然后,将不同阶段的特征聚合成视频级特征。此外,将改进后的帧级特征聚合为质量更高的改进步态相位特征,用于重新改进帧级特征。因此,我们将GPS升级为迭代步态周期集(IGPS),迭代地细化帧级特征。在主流步态识别数据集上的大量实验结果验证了GPS和IGPS模块的有效性,并表明所提出的方法达到了最先进的性能。
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CiteScore
10.90
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