KEYWORDS: Convolution, RGB color model, Bone, Data modeling, Video, Performance modeling, Detection and tracking algorithms, Visualization, Video surveillance, Neural networks
Skeleton-based human action recognition has achieved a great interest in recent years. The strong robustness of skeleton data to scene and camera interference makes the recognition algorithm follow with interest robust features of actions. Recent works has proved the effectiveness of skeleton modeling based on graph and learning spatio-temporal modes by Graph Convolutional Network (GCN). Although GCNs have excellent ability of neighborhood feature learning, it is not good at capturing long-distance dependence between joints. In particular, linear temporal skeleton sequences contain a great quantity of joints., which makes the process of learning advanced temporal cues unduly slow. In this paper, we propose a temporal feature enhancer using Temporal Kernel Attention (TKA). And guided by TKA, we design a performance-oriented network TKA-GCN and a lightweight network Mini-TKA-GCN for skeleton-based action recognition. Finally, on NTU-RGBD 60 and Kinetics-Skeleton 400 datasets, TKA-GCN and Mini-TKA-GCN proposed by this work, outperform most advanced works.
3D point cloud simplification is an important pre-processing task in geometric reconstruction to save computer resources and increase reconstruction speed. Compared with traditional methods, the existing point cloud simplification algorithm based on k-means has a better effect on the extraction and retention of feature points, while there will be many larger holes near the feature points. In this paper, we propose a scattered point cloud simplification algorithm (SPSA). The SPSA strikes a balance between retaining enough feature points and keeping the sample points evenly distributed. The SPSA uses boundary search and the fast point feature histogram to extract feature points. Further, we apply the improved farthest point sampling algorithm to sample non-feature regions to ensure the integrity of the simplified model. Extensive experiment results on standardized information entropy evaluation show that the SPSA outperforms other simplification algorithms. In addition, the visualization results indicate that the proposed method can ensure the integrity of the simplified model and retain the most of feature information of the point cloud.
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