Co-Vision: Automated Video Context Understanding and Classification Using AI

Abstract

In today's digital landscape, a challenge is presented by the ever-growing volume of video content: how to efficiently understand, access, and utilize this wealth of information. This challenge is addressed through developing an innovative web application combining video understanding, content recommendation, and accessibility features. The web application's core is found in its ability to be automatically analyzed and comprehended by videos' content. Valuable insights are extracted, key events, objects, and actions are identified, and textual explanations are generated, all of which are leveraged by state-of-the-art machine learning techniques in computer vision and natural language processing. The comprehension of video content forms the foundation for two significant applications, such as content recommendation, in which the content of videos and user preferences are understood, and our application revolutionizes content recommendation systems. Personalized video recommendations are provided, ensuring that content is discovered by users that precisely match their interests and needs. This user experience enhancement, along with resulting higher user engagement and content consumption on recommendation-driven platforms, is achieved. Accessibility is increased as the project promotes inclusivity by making video content more accessible to a broader audience. For individuals with disabilities, textual explanations and transcripts are generated by our application, breaking down barriers to understanding. Additionally, the convenience of summarizing lengthy videos is offered, enabling users to quickly grasp key insights without the need to watch the entire content. This enhanced accessibility is particularly valuable in educational contexts and beyond, where content consumption is made more efficient and equitable.