In skeleton-based human action recognition, spatial-temporal graph convolution networks (ST-GCNs) have achieved remarkable performances recently. However, how to explore more discriminative spatial and temporal features is still an open problem. The temporal graph convolution of the traditional ST-GCNs utilizes only one fixed kernel which cannot completely cover all the important stages of each action execution. Besides, the spatial and temporal graph convolution layers (GCLs) are serial connected, which mixes information of different domains and limits the feature extraction capability. In addition, the input features like joints, bones, and their motions are modeled in existing methods, but more input features are needed for better performance. To this end, this article proposes a novel multi-stream and enhanced spatial-temporal graph convolution network (MS-ESTGCN). For each basic block of MS-ESTGCN, densely connected multiple temporal GCLs with different kernel sizes are employed to aggregate more temporal features. To eliminate the adverse impact of information mixing, an additional spatial GCL branch is added to the block and the spatial features can be enhanced. Furthermore, we extend the input features by employing relative positions of joints and bones. Consequently, there are totally six data modalities (joints, bones, their motions and relative positions) that can be fed into the network independently with a six-stream paradigm. The proposed method is evaluated on two large scale datasets: NTU-RGB+D and Kinetics-Skeleton. The experimental results show that our method using only two data modalities delivers state-of-the-art performance, and our methods using four and six data modalities further exceed other methods with a significant margin.

The emergence of low-cost depth sensors opens up new potentials for skeleton-based human action recognition. The recent methods for this task have made significant progress by incorporating graph convolution. However, they (1) have limitations in modeling the complex and variable temporal dynamics, and (2) cannot maximize the complementarity of the spatial and temporal features. Besides, (3) the loss function of these methods has an inherent weakness in optimizing the intraclass compactness. To this end, we propose a pyramidal graph convolutional network (PY-GCN) in this paper. Specifically, (1) an effective yet efficient single-oriented pyramidal convolution is proposed. It involves multiple kernels with varying sizes and depths that are capable of capturing different levels of the temporal dynamics at multiple scales. (2) A pseudo-two-stream structure for the basic block of the network is proposed to comprehensively aggregate discriminative cross-spatiotemporal features. Moreover, (3) a pairwise Gaussian loss together with the cross-entropy loss is introduced to the model, which can focus on both intraclass compactness and interclass separability. Our PY-GCN achieves state-of-the-art performance on three challenging large-scale datasets.

In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the temporal cues comprehensively. Moreover, simply connecting the spatial graph convolution layer (GCL) and the temporal GCL in series is not the optimal solution. To this end, we propose a novel enhanced spatial and extended temporal graph convolutional network (EE-GCN) in this paper. Three convolution kernels with different sizes are chosen to extract the discriminative temporal features from shorter to longer terms. The corresponding GCLs are then concatenated by a powerful yet efficient one-shot aggregation (OSA) + effective squeeze-excitation (eSE) structure. The OSA module aggregates the features from each layer once to the output, and the eSE module explores the interdependency between the channels of the output. Besides, we propose a new connection paradigm to enhance the spatial features, which expand the serial connection to a combination of serial and parallel connections by adding a spatial GCL in parallel with the temporal GCLs. The proposed method is evaluated on three large scale datasets, and the experimental results show that the performance of our method exceeds previous state-of-the-art methods.

Overlying hard and thick igneous rocks pose numerous potential safety hazards during the exploitation of coal resources. Identifying the spatial distribution of igneous rocks and analyzing their impact on coal mining are a primary research concern. In this study, a coal mine was investigated in depth. Initially, based on the seismic information, the authors predicted the occurrence conditions of igneous rocks in coal measure strata. Subsequently, two models were developed via the UDEC software4.00: one with igneous layers and the other without. Using the simulation results, the change law of stress, the roof abscission layer, and roof strata subsidence in the overburden during coal face advancement were analyzed. Through a comparison of the simulation results, the hazard-causing mechanism of the igneous intrusion was discussed. Consequently, the occurrence of igneous rocks in the overburden is crucial for predicting potential safety hazards, and the seismic inversion method can be considered an effective tool for evaluating overlying igneous strata.