基于相对骨骼点特征和时序自适应感受野的动作识别方法

Action Recognition Based on Relative Skeleton Point Features and Temporal Adaptive Receptive Field

针对长时间动作识别难以充分利用时空域信息的问题,提出了基于相对骨骼点特征和时序自适应感受野的动作识别方法。首先,该方法在特征获取部分增加了相对骨骼点特征,以满足节点多样性和互补性要求,将其分别输入到空域图卷积网络,获得空间中相邻关节聚合的局部特征。然后,设计了一个时序自适应感受野网络,以获取在时域中关节变化的局部特征,并且增加了网络对不同持续时长动作的适应性。最后,经过决策级融合模块,计算类别概率,得到分类结果。仿真结果表明,基于NTU RGB+D和Kinetics-skeleton两大基准数据集,对比多种主流方法,均取得了更高的识别准确率,分别为96.2%与60.1%。该方法可以较好地提取不同动作的区别性时间特征,提高了动作时空特征的判别能力。

Aiming at the problem that it is difficult to make full use of temporal and spatial domain information for long-term action recognition,an action recognition method based on relative skeleton point features and temporal adaptive receptive field is proposed.Firstly,this method adds relative skeleton point features in the feature acquisition part to meet the node diversity and complementarity requirements,and respectively inputs them into the spatial graph convolution network to obtain the local features of the adjacent joint aggregation in space.Then,a time-series adaptive receptive field network is designed to obtain the local features of joint changes in the time domain,and to increase the adaptability of the network to actions of different durations.Finally,through the decision-level fusion module,the category probability is calculated to obtain the classification result.The simulation results show that based on the two benchmark datasets of NTU RGB+D and Kinetics-skeleton,compared with other mainstream methods,this method has achieved higher recognition accuracies,which are 96.2%and 60.1%,respectively.The method can better extract the discriminative temporal features of different actions,and improve the discrimination ability of spatiotemporal action features.