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收稿日期：2024-11-18，
修回日期：2025-01-12，
录用日期：2025-01-16，
网络出版日期：2025-01-23，
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目的
2
作为一种生物特征，步态可以通过行走时的步态习惯来区分目标身份。但由于视频数据的复杂性，隐藏在连续帧中的不受外部协变量干扰的运动状态很难被直接捕获。针对该问题，本文提出了一种基于局部时空卷积的步态识别方法来自主地学习步态运动模式。
方法
2
受分块思想的启发，提出了全局双向空间池化方法使步态张量降维，以局部条带为基本单位表示步态特征空间上的细节。并且设计了局部时空卷积层，使时域和空间域上的信息共同参与卷积运算，从而让二维卷积层能够自适应地学习基于条带的步态运动。并且通过非对称卷积分别关注时域、空间域和时空域，从而能够更好地提取步态时空特征。另外，提出了基于局部的时空池化方法，能够融合多帧中最有区分性的局部步态时空表征，以生成更具身份判别性的步态特征。
结果
2
所提出的方法在两个基准的公共数据集上的实验均取得了最高识别精度，在CASIA-B数据集上的三种行走条件下分别达到了97.3%、93.7%和83.8%的平均识别正确率，在OU-MVLP上取得了85.8%的平均识别结果，证明了所提方法的优越性。
结论
2
本文所提出的局部时空卷积网络的方法，有较好的时空域特征学习能力，能够提升步态识别准确率。
Objective With the development of technology and the expansion of application scenarios， biometric recognition technology is gradually becoming one of the mainstream technologies in the field of future security authentication. Gait is a biometric property that can be utilized to distinguish target objects based on their walking patterns. However， capturing the motion pattern hidden within a series of frames is challenging due to the complexity of video data. Existing gait recognition methods suffer from difficulties in learning gait motion habits under a wide range of recognition conditions and achieving excellent real-time performance， due to the influence of random and diverse external factors such as complex backgrounds， pedestrian clothing， walking directions， and illumination changes. To address this issue， we propose a gait recognition method based on the local spatiotemporal convolutional network to autonomously learn gait motion patterns.
Method
2
Unlike introducing manual motion features， this network directly endows the two-dimensional convolutional network with the ability to extract temporal information. It adaptively learns complex underlying structures and patterns in video data driven by data to capture action features， and can adapt to different gait data through continuous learning， making the model highly universal. Specifically， inspired by the partitioning method， a global bidirectional spatial pooling method was proposed to decrease the dimensionality of gait tensors， and local strips were employed as the fundamental units to describe the details in the gait space. By using global bidirectional spatial pooling， gait features are divided into horizontal and vertical local features in the spatial domain， and the method of partitioning is utilized to pay attention to gait details while reducing dimensionality. In this way， the local spatiotemporal convolutional layer was designed to integrate the spatial and temporal domains， allowing for adaptive learning of strip-based gait motion. The local spatiotemporal convolution attempts to involve the spatial domain， channel domain， and time domain in two-dimensional convolution operations. This novel layer allows both the temporal and spatial dimensions to participate in the learning of the convolutional network， enabling the two-dimensional convolutional structure to capture spatiotemporal features of gait. Asymmetric convolution can also be extended to local spatiotemporal convolution， constructing asymmetric local spatiotemporal convolution layers. Asymmetric convolution can explicitly enhance the representational power of standard square convolution kernels， and integrating asymmetric convolution can better extract spatial features. In addition， a local spatiotemporal pooling method can combine the discriminative local gait spatiotemporal representations from multiple frames to generate more discriminative gait features. By this means， the dimensionality reduction of the gait tensor is achieved， making it possible for the time-domain dimension to participate in the calculation of two-dimensional convolution， and the spatial representation details of gait are integrated， reducing the loss of spatial features.
Result
2
Extensive experiments on gait benchmark datasets have demonstrated the effectiveness of various parts of the designed network， and comparisons with other methods have also demonstrated the superiority of this approach. The experiments on two benchmark public datasets show that the proposed method outperforms other current gait recognition approaches. The method proposed in this article achieved the best recognition performance under three training settings on the CASIA-B dataset. It achieves average recognition accuracies of 97.3%， 93.7%， and 83.8% under three walking scenarios， respectively， and 85.8% on OU-MVLP. Moreover， the comparison with other recent methods proves the superiority of this method.
Conclusion
2
The experimental results show that the local spatio-temporal convolutional network has excellent spatio-temporal feature learning capacity and has a positive effect on improving gait recognition performance. These findings demonstrate that the local spatiotemporal convolutional network can effectively learn the spatiotemporal features and show the superiority of the proposed method， and have good spatiotemporal feature learning ability and has a positive effect on improving gait recognition performance. Therefore， the proposed local spatiotemporal convolutional network can adaptively capture spatiotemporal features of gait， providing a new method for research in the field of gait recognition.
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