基于骨架坐标的LC融合动作识别算法

LC fusion action recognition algorithm based on skeleton coordinates

动作识别是计算机理解人类行为的关键技术,为了提高动作识别算法的时空特征提取能力,本文提出了一种融合LSTM和CNN的动作识别算法。该算法利用LSTM子网捕捉时间信息,采用CNN子网捕捉空间特征,然后融合特征进行动作识别。本文方法在NTU RGB-D数据集上,CS验证的准确率达到87.0%,CV验证的准确率达到91.5%。此外,针对动作时间长度不统一问题,同时对比了近邻插补和零向量插补方法,得到前者表现较优的结论。

Action recognition is a key technology for computers to understand human behavior.To improve the ability of extracting spatio-temporal features of action recognition algorithms,this paper proposes an action recognition algorithm that integrates LSTM and CNN.LSTM subnet captures temporal information,CNN subnet captures spatial features,and then fuses features for action recognition.The accuracy of CS verification on NTU RGB-D dataset and CV verification is 87.0%and 91.5%respectively.Aiming at the problem of non-uniform action time length,this paper compares the nearest neighbor interpolation and zero vector interpolation,and concludes that the former performs better.