Abstract:Aiming at the most important motion characteristics of human behavior, a second-level recursive anomaly behavior recognition method based on time context is proposed. Different from traditional deep learning training methods, this method does not directly learn features from image data, but extracts them. The shape information HOG feature is used as the training input. Firstly, the image shape feature based on the HOG algorithm is extracted, and the extracted feature is used to train the DBN network. Secondly, the trained DBN network and the Softmax classifier are used to identify the human body coarse target region, and then according to the coarse The time-series context information of the target area, calculate the centroid acceleration. Finally, the threshold of the acceleration is judged, and the precise target area of the abnormal behavior is identified. This paper applies the two-level recursive method combining the weight and the target to the classroom behavior recognition, and the experimental results show that the The method can better recognize the classroom behavior in the scenes of motion blur and target dense occlusion, and the recognition rate is greatly improved compared with other methods. Classroom abnormal behavior data analysis can play a supporting role in classroom dynamic management and learning effect evaluation.