LLM VisionAI: Optimizing Video Analysis

The "LLM VisionAI" capstone project, undertaken by students in the Computer Science and Engineering program at Ohio State University, aims to enhance video analysis through the integration of a Large Language Model (LLM) into a video processing pipeline. This project addresses the challenge of minimizing unnecessary data storage by automatically filtering out uneventful footage. Structured in three phases, the project focuses on creating a scalable, efficient solution for video monitoring systems, ultimately reducing storage requirements and improving the efficiency of video analysis.

In the first phase, the team developed a Dockerized environment to ensure compatibility across different systems and to streamline the deployment process. Docker was chosen for its ability to create lightweight, portable containers that can run consistently on any environment, making it ideal for handling the various components of the video processing pipeline. This foundational step ensured that the system could be easily deployed and maintained, providing a robust framework for the subsequent phases of the project.

The second phase involved integrating a Large Language Model, specifically LLaVA and GPT-4, to generate textual descriptions of video content. By leveraging the advanced capabilities of these models, the system can interpret and categorize video data more accurately. This integration allows the system to process and analyze video footage in real-time, identifying key events and filtering out irrelevant or uneventful segments. The use of LLMs significantly enhances the system's ability to understand and respond to complex visual information, setting a new standard for automated video analysis.

In the final phase, the team developed a system to flag potential road dangers using the processed video data. This involved creating algorithms to detect and highlight hazardous situations, such as obstacles or erratic driving behavior. The flagged footage is then prioritized for review, ensuring that critical incidents are quickly identified and addressed. This feature not only enhances the safety and reliability of the video monitoring system but also reduces the time and resources required for manual video review.

Throughout the project, the team utilized a range of technologies, including Python, OpenCV, MongoDB, and Flask, to build a comprehensive and efficient system. The user interface was designed to provide a seamless experience, allowing users to easily start recordings, upload files, and view analyzed footage. The project demonstrated the potential for LLMs to revolutionize video processing, offering a scalable solution that can be adapted for various applications, from security surveillance to autonomous vehicle monitoring. The team's innovative approach and use of cutting-edge technologies highlight the potential for AI-driven systems to transform how we manage and interpret visual data, paving the way for future developments in automated video monitoring.