Detection of Abnormal Behavioral States in Student Learning Based on Video Surveillance

The core of student learning behavior state detection and abnormal behavior detection lies in feature extraction and classifier design. This article proposed an application scheme for detecting abnormal behavior states in student learning based on video surveillance, which improved the efficiency and accuracy of monitoring work and provided new ideas and methods for school education management. This article mainly applied experimental design and algorithm comparison to conduct behavior detection. The experimental results showed that after multiple model training and testing, the accuracy of the model reached over 90%. In order to improve the accuracy and robustness of the detection algorithm, techniques such as data augmentation and cross validation can also be used to optimize the algorithm.