基于骨骼的双支融合模型的人体行为识别

Human behavior recognition based on double-branch fusion model based on skeleton

针对循环神经网络存在提取特征单一,对特征的空间信息处理不充分的问题,提出一种基于骨骼的双支融合的人体行为识别模型。该模型由双向循环门网络和多尺度的残差网络融合的双支网络中进行特征提取,得到丰富的时间和空间上的特征信息,并且在双向循环门网络中增加注意力机制,进一步提升整个网络的性能,最后将特征信息经过分类器进行分类得到动作。分别使用UCF101和HMDB51数据集进行实验,准确率分别为98.0%和67.8%。通过实验测试,证明该模型能够获得更加完整的特征信息并且具有良好的性能指标。

Aiming at the problem that the recurrent neural network has a single feature extraction and insufficient processing of spatial information of the feature, a two-branch fusion human behavior recognition model based on bone is proposed. The model is extracted by the two-branched network of two-way cyclic gate network and multi-scale residual network, which obtains rich feature information in time and space, and increases the attention mechanism in the bidirectional cyclic gate network to further improve the performance of the whole network, and finally the feature information is classified through the classifier to obtain action. Experiments were conducted using the UCF101 and HMDB51 datasets, respectively, with an accuracy rate of 98.0% and 67.8%, respectively. Through experimental tests, it is proved that the model can obtain more complete feature information and has good performance indicators.