# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
###### Abstract
[6] introduced the two-stream adaptive graph convolutional network (2s-AGCN), which successfully utilized the length and direction features of skeleton data to enhance the precision of human action recognition. Furthermore, some human skeleton action identification techniques utilizing GCN have demonstrated impressive results in references [2; 7; 8]. Consequently, the utilization of GCN for extracting features from human skeleton data has progressively emerged as the predominant approach in the domain of human action recognition. Currently, human skeleton action recognition algorithms that rely on GCN are still encountering certain difficulties:  
* The graph topology of the network is predetermined, shared, and remains constant. The model's limitations include a lack of flexibility and the inability to accurately represent the various levels of semantic information in human activities. Consequently, it encounters challenges in precisely depicting the interrelationships among the joints of the human body. The interdependence of joints is quite intricate and fluctuates depending on various actions. The adjacency matrix shared in the learning channel equally makes it difficult to fit these correlations, which makes the model lose the ability to distinguish the subtle differences between different actions. * In human skeleton action recognition, the local receptive field is closely related to the body's physical structure, and different joint points have different numbers of neighborhood nodes. However, the aggregation method in the existing methods only simply aggregates the acquired features, resulting in a limited receptive field. Therefore, it is difficult for the model to effectively reflect the diverse relationships between long and short ranges within skeletons. This study introduces an ACE-GCN for the purpose of skeleton human action recognition. The proposed network aims to address the aforementioned issues to a certain degree. The ACE-GCN can enhance the representation capability of GCNs by providing a more flexible and discriminative feature learning process. The main goal of this study is to create a relevance matrix and affinity matrix of the graph topology using the attention mechanism. This matrix utilizes not only the local information in each channel but also the global contextual information across channels. This approach further refines the graph topology in each channel by adaptively assigning weights to all channels. Since the adjacency matrix in each channel is adaptively non-shared, the aggregation of redundant information is avoided. In addition, this study integrates Edgeconv [9] into the model, constituting a multi-branch aggregation module. Edgeconv captures local geometric features in the spatial domain by dynamically constructing the neighborhood relations of points, while other parallel aggregation branches capture the spatio-temporal features of the joints globally. The utilization of several branches in this aggregation strategy improves the extraction of spatio-temporal information at various scales with the model. The experimental findings from the NTU RGB+D 60 dataset [10], NTU RGB+D 120 [11] dataset, and NW-UCLA dataset [12] demonstrate that the suggested model achieves superior accuracy while requiring fewer computer resources. The innovative work of this study is as follows:  
* In this study, a novel adaptive channel-enhanced graph convolution network (ACE-GCN) is proposed. The ACE-GCN utilizes a channel attention mechanism to dynamically construct adaptive and non-shared channel-based adjacency matrices, which achieves topology refinement in each feature channel and enables the model to capture effective correlations between joints in different actions. * In this study, Edgeconv is integrated to form a multi-branch aggregation method, which can effectively aggregate spatio-temporal features from different global-local scales in spatial and temporal domains.