Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

Abstract

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed to enhance the features of key bone points, and Softmax is used to classify and identify dangerous behaviors. The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets. Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building, and its accuracy is higher than those of other similar methods.

Multimodal audio-visual robot fusing 3D CNN and CRNN for player behavior recognition and prediction in basketball matches

“…Notably, it exhibits superior results in terms of training time, inference time, and model complexity when compared to existing methods, namely CNN-LSTM (Tay et al, 2019 ), CNN-BiLSTM (Halder and Chatterjee, 2020 ), LSTM-GCN (Zhao et al, 2023 ), and LSTM-GANs (Rossi et al, 2021 ). In terms of training time, our model requires significantly less time, achieving a training time of 800 seconds for NBA PTD and 700 seconds for SD, outperforming other methods by a substantial margin.…”