Research on Intelligent Batch Evidence Collection for Video Duration

Abstract

This paper investigated the intelligent batch evidence collection technology for video duration. With the rapid growth of digital video content, accurately obtained and analyzed video duration has become a significant challenge. Fields such as judiciary, copyright protection, and advertising monitoring urgently require efficient and accurate batch video duration collection procedures. This paper discussed key technical points of intelligent batch extraction of video duration, including common video formats, intelligent analysis technologies, and applications of big data analysis techniques. It implemented automatic identification of video formats through file extension analysis and file header detection and designed a module for extracting video duration. Additionally, video content analysis algorithms were optimized to enhance processing speed and accuracy. During the data preprocessing phase, video duration data was cleaned, organized, deduplicated, and supplemented for missing values, constructing a high-quality video duration dataset. By employing statistical analysis and data mining methods, the paper delved into video duration data and provided detailed data support for case handling. The results were visualized to offer intuitive support for decision-making. The research findings were summarized, with existing problems and directions for improvement pointed out, along with suggestions for future research, including continuous optimization of algorithms, models, or system architectures, and the introduction of cloud computing and big data technologies to build efficient and scalable video evidence collection platforms, as well as exploring cross-platform video processing and analysis solutions and more applications of intelligent technologies.