基于动态拓扑图的人体骨架动作识别算法

Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph

传统的人体骨架动作识别算法采用手动构建拓扑图的方式来建模包含在多个视频帧中的动作序列,并针对性地学习每个视频帧以反映数据变化,这容易造成计算代价大、网络泛化性低和灾难性遗忘等问题。针对上述问题,提出了基于动态拓扑图的人体骨架动作识别算法,使用持续学习思想动态构建人体骨架拓扑图。将具有多关系特性的人体骨架序列数据重新编码为关系三元组,并基于长短期记忆网络,通过解耦合的方式学习特征嵌入。当处理新骨架关系三元组时,使用部分更新机制动态构建人体骨架拓扑图,并采用基于时空图卷积网络的骨架动作识别算法来实现动作识别。实验结果表明,所提方法在Kinetics-Skeleton,NTU-RGB+D(X-Sub)和NTU-RGB+D(X-View)基准数据集上分别取得了40%,85%和90%的识别准确率,提高了人体骨架动作识别的准确率。

Traditional human skeleton action recognition algorithms manually construct topological graphs to model the action sequence contained in multiple video frames and learn each video frame to reflect the data changes,which may lead to the high computational cost,low network generalization performance and catastrophic forgetting.To solve these problems,a human skeleton action recognition algorithm based on dynamic topological graph is proposed,in which the human skeleton topological graph is dynamically constructed based on continuous learning.Specifically,human skeleton sequence data with multi-relationship characte-ristics are recoded into relationship triplets,and feature embedding is learned in a decoupling manner via the long short-term me-mory network.When handling new skeleton relationship triplets,we dynamically construct the human skeleton topological graph by a partial update mechanism,and then send it to the skeleton action recognition algorithm based on spatio-temporal graph convolution network for action recognition.Experimental results demonstrate that the proposed algorithm achieves 40%,85%and 90%recognition accuracy on three benchmark datasets,namely Kinetics-Skeleton,NTU-RGB+D(X-Sub)and NTU-RGB+D(X-View),respectively,which improve the accuracy of human skeleton action recognition.