Gait recognition has received widespread attention due to its non-intrusive recognition mechanism. Currently, most gait recognition methods use appearance-based recognition methods, and such methods are easily affected by occlusions when facing complex environments, which in turn affects the recognition accuracy. With the maturity of pose estimation techniques, model-based gait recognition methods have received more and more attention due to their robustness in complex environments. However, the current model-based gait recognition methods mainly focus on modeling the global feature information in the spatial dimension, ignoring the importance of local features and their influence on recognition accuracy. Meanwhile, in the temporal dimension, these methods usually use single-scale temporal information extraction, which does not take into account the inconsistency of the motion cycles of the limbs when a human body is walking (e.g., arm swing and leg pace), leading to the loss of some limb temporal information. To solve these problems, we propose a gait recognition network based on a Global–Local Graph Convolutional Network, called GaitMGL. Specifically, we introduce a new spatio-temporal feature extraction module, MGL (Multi-scale Temporal and Global–Local Spatial Extraction Module), which consists of GLGCN (Global–Local Graph Convolutional Network) and MTCN (Multi-scale Temporal Convolutional Network). GLGCN models both global and local features, and extracts global–local motion information. MTCN, on the other hand, takes into account the inconsistency of local limb motion cycles, and facilitates multi-scale temporal convolution to capture the temporal information of limb motion. In short, our GaitMGL solves the problems of loss of local information and loss of temporal information at a single scale that exist in existing model-based gait recognition networks. We evaluated our method on three publicly available datasets, CASIA-B, Gait3D, and GREW, and the experimental results show that our method demonstrates surprising performance and achieves an accuracy of 63.12% in the dataset GREW, exceeding all existing model-based gait recognition networks.

Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness.

A new trend in long-range biometrics, gait recognition, is finding application in a number of different fields including video surveillance. Recently, with the increase in robustness of the pose estimator and the presence of various unpredictable factors in realistic gait recognition, skeleton-based methods with higher robustness have emerged to better meet the challenging gait recognition needs. However, existing approaches primarily focus on extracting global skeletal features, neglecting the intricate motion information of local body parts and overlooking inter-limb relationships. Our solution to these challenges is the dynamic local fusion network (GaitDLF), a novel gait neural network for complex environments that includes a detail-aware stream in addition to the previous direct extraction of global skeleton features, which provides an enhanced representation of gait features. To extract discriminative local motion information, we introduce predefined body part assignments for each joint in the skeletal structure. By segmenting and mapping the overall skeleton based on these limb site divisions, limb-level motion features can be obtained. In addition, we will dynamically fuse the motion features from different limbs and enhance the motion feature representation of each limb by global context information and local context information of the limb-level motion features. The ability to extract gait features between individuals can be improved by aggregating local motion features from different body parts. Based on experiments on CASIA-B, Gait3D, and GREW, we show that our model extracts more comprehensive gait features than the state-of-the-art skeleton-based method, demonstrating that our method is better suited to detecting gait in complex environments in the wild than the appearance-based method.

Traffic forecasting’s key challenge is to extract dynamic spatial-temporal features within intricate traffic systems. This paper introduces a novel framework for traffic prediction, named Local-Global Spatial-Temporal Graph Convolutional Network (LGSTGCN). The framework consists of three core components. Firstly, a graph attention residual network layer is proposed to capture global spatial dependencies by evaluating traffic mode correlations between different nodes. The context information added in the residual connection can improve the generalization ability of the model. Secondly, a T-GCN module, combining a Graph Convolution Network (GCN) with a Gated Recurrent Unit (GRU), is introduced to capture real-time local spatial-temporal dependencies. Finally, a transformer layer is designed to extract long-term temporal dependence and to identify the sequence characteristics of traffic data through positional encoding. Experiments conducted on four real traffic datasets validate the forecasting performance of the LGSTGCN model. The results demonstrate that LGSTGCN can achieve good performance and be applicable to traffic forecasting tasks.

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios.