融合运动轨迹特征的多模态群体行为识别方法

Multi-modal group activity recognition method combining motion trajectory features

群体行为识别从群体层面出发,研究群体的行为及个体的动作并进行分类.准确的群体行为识别结果对安防监控、体育视频分析等领域有重要意义.针对目前基于LSTM的群体行为识别无法充分挖掘个体间在群体层面时空特征的问题,提出一种基于LSTM-Transformer的群体-个体时空特征融合群体行为识别模型.在此基础上,首次将运动轨迹特征融入群体行为识别中,提出融合运动轨迹特征的群体行为识别模型,进一步提升模型的识别效果.实验结果表明,相比现有基于LSTM的模型,所提出模型的群体行为识别准确率提升8.3%,个体动作识别准确率提升2.1%;相比基于GCN的模型,所提出模型不仅识别效果有所提升,而且可应对群体人数变化的场景.

Group Activity Recognition focused on group activities and individual actions classification from a group level perspective.A better group activity recognition result is of great significance to applications such as security monitoring and sports video analysis.To deal with the problem that current LSTM based models could not fully extract spatial-temporal features at group level,a LSTM-Transformer based group activity recognition model was proposed to utilize group-individual features.Additionally,a multi-modal model combining trajectory features was proposed for the first time at group activity recognition.The experimental results show that compared with the existing LSTM-based models,the accuracy of the proposed model's group activity recognition is increased by 8.3%,and the accuracy of individual action recognition is increased by 2.1%.Compared with the GCN-based model,the proposed model not only improves the recognition accuracy,but also can handle group with varying size.