Abstract: 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.