Analysis of Art Classroom Teaching Behavior Based on Intelligent Image Recognition

To solve the problem of intelligent image recognition in classroom behavior, this paper proposes a fast target detection based on FFmpeg CODEC, extracts MHI-HOG joint features according to the located foreground target area, and finally completes the behavior recognition model through a BP neural network support vector machine joint classifier based on the look-up table. The results are as follows: the motion detection method based on H.264 FFmpeg CODEC video has the highest detection accuracy, which can reach 95%. The foreground detection method takes about 10 ms and saves 90% of the time. The behavior classification and recognition effect of MHI-HOG joint features based on the model has been significantly improved, and the comprehensive recognition rate has reached 95%. The built-in BP neural network support vector machine has 97% accuracy in extracting, classifying, and recognizing the characteristics of a single sample. This study attempts to identify and analyze the class behavior and validate the effectiveness of the collaborative classifiers proposed in this paper to build an intellectual classroom.