Wang Yulin, Song Tao, Yang Yichen, Hong Zheng
College of Intelligent Transportation, Chongqing Vocational College of Public Transportation, Chongqing 402260, China.
College of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China.
Sensors (Basel). 2024 Nov 28;24(23):7595. doi: 10.3390/s24237595.
Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi-granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a accuracy of 95.67%, which is superior to existing behavior recognition methods.
针对现有人体骨架行为识别方法对人体局部动作不敏感、在区分相似行为时识别不准确的问题，提出了一种融合多粒度特征的多尺度时空图卷积方法用于人体行为识别。首先，提出一种骨架细粒度划分策略，将骨架数据初始化为不同粒度的数据流。利用归一化高斯函数设计自适应跨尺度特征融合层，在不同粒度之间进行特征融合，通过细粒度特征引导模型关注相似行为之间的判别性特征表示。其次，引入稀疏多尺度邻接矩阵，解决多粒度条件下多尺度空间域建模过程中放大的偏差加权问题。最后，构建端到端的图卷积神经网络，提高时空感受野信息的特征表达能力，增强相似行为之间识别的鲁棒性。在公开行为识别数据集MSR Action 3D上验证了所提算法的可行性，准确率达到95.67%，优于现有行为识别方法。
