Abstract
Human behaviour recognition is an important research hotspot in the field of artificial intelligence. Current behaviour recognition methods have low recognition accuracy under different viewing angles, therefore, this paper proposes a novel restricted Boltzmann machine (RBM)-based temporal-spatial correlation method for student behaviour recognition in depth video. The RBM is used to map the human behaviour from different viewing angles to the high-dimensional space. The time level pooling function is applied in the time series activated by each neuron to realise the encoding of the video time sub-series. Finally, behaviour recognition and classification experiments are conducted on different public datasets and real classroom student behaviour datasets with other methods. The results show that the proposed method improves the accuracy of depth video recognition under different viewing angles and has good generalisation performance. The data analysis of abnormal behaviour in class can play an auxiliary role in dynamic classroom management.
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