Development and Application of an Automatic Recognition Model for Film and Television Education of Movie Shot Category

In film and television education, shot scale identification is a key link in developing students' visual literacy and narrative understanding. However, in teaching, shot scale recognition mainly relies on Professors' manual labeling in advance, which has the problems of low automation, high subjectivity, and insufficient efficiency, limiting the depth and breadth of teaching and research. This study, based on the MovieShots Dataset, designed and developed an automatic shot scale recognition model using deep learning. It utilizes YOLOv5 to achieve the automatic classification of Long Shot (LS), Full Shot (FS), Medium Shot (MS), Close-Up(CU), and Extreme Close-Up(ECU). Additionally, it combines PySceneDetect technology for shot segmentation and video analysis. The experimental results indicate that the model's average precision (mAP@0.5) on the test set reaches 85.7, significantly improving the analysis efficiency. Subsequently, the model was applied in a simulated cinematic education classroom where one instructor and ten students utilized it for shot scale recognition. According to the experimental results, the model recognition effect and the Professor recognition results reached 90% recognition accuracy, and the time efficiency was greatly improved. At the same time, according to the results of the questionnaire survey, students have a better acceptance of using the model for teaching assistance. At the same time, according to the subjective interview feedback, Professors and students tend to use this efficient automatic learning aid to reduce the burden of manual annotation.