Computer Vision Driven Film and TV Scene Modeling and Innovation

Abstract

The expanding capabilities of computer vision technology now enable the extraction, analysis, and comprehension of high-level information from images and videos. This advancement opens up vast possibilities for innovation and technical enhancements in film and TV production. In the realm of film and TV scene modeling, computer vision algorithms offer significant improvements in accuracy and efficiency. Furthermore, these algorithms, coupled with data mining (DM) techniques, introduce refreshing artistic and visual components to scene design. This paper presents a novel approach that integrates Genetic Algorithms (GA) to optimize Convolutional Neural Networks (CNN), enhancing the effectiveness of computer vision algorithms in CAD modeling for film and TV scenes. The combination of GA and CNN leverages GA’s global search capability to refine the network structure and parameter configuration of CNN, while GA’s parallel computing power expedites the training of CNN models. The results show that GA-CNN can learn and adapt to different data distribution and noise conditions through GA optimization and has strong robustness and generalization ability. This means that GA-CNN can maintain high detection accuracy even in the face of scenes that have not appeared in the training set.