Development of an AI-Powered Multimedia Search Engine for Archival Media Accessibility

The client manages a vast archive of audiovisual media, often in monochrome or low-quality formats, with metadata primarily entered manually. They face difficulties in efficiently locating specific scenes, objects, or audio elements within their collections, limiting accessibility and user experience. The lack of automated recognition tools hampers content discovery and archival research efforts.

A large-scale media production or archival institution seeking to enhance media search capabilities within extensive audiovisual collections.

• Analyze and process extensive audiovisual archives, including monochrome and low-quality media, to automatically identify and label key elements such as objects, actors, locations, sounds, and emotions.
• Develop a robust, scalable AI-driven search engine capable of delivering precise and rapid search results for complex queries across large media datasets.
• Enhance existing media metadata with automatically generated tags to improve searchability and user navigation.
• Achieve high accuracy in object, person, location, and sound recognition, aiming for minimal error rates, and handle diverse media formats and qualities.
• Enable users to perform complex, multi-criteria searches for scenes containing specific objects, individuals, sounds, or locations, including both visual and audio elements.

• Automated recognition and tagging of objects, actors, locations, sounds, and emotional cues within media files.
• Advanced API for communication between media analysis components and data management systems.
• User interface enabling complex search queries combining visual and auditory criteria.
• Capability to process large volumes of media content efficiently, including archival, monochrome, and low-quality formats.
• Tools for calibrating AI models to improve recognition accuracy over iterative processing cycles.
• Error handling, logging, and system health monitoring features to ensure high availability and reliability.

• Existing media asset management systems for media ingestion and storage.
• Metadata databases for enriching and updating media descriptions.
• User interface layers or content portals to facilitate user interaction with search features.

• System must process at least 36,000 minutes of media and analyze upwards of 70,000 objects within a 4-month timeframe.
• Recognition accuracy should aim for a low error rate, with continuous model calibration to improve performance over time.
• High system availability with minimal downtime, supported by error logging and automated health checks.
• Secure handling of media content, with compliance to data privacy standards as applicable.
• Scalability to accommodate increasing media volumes and evolving recognition capabilities.

This AI-powered multimedia search engine is expected to substantially improve media content discoverability, reducing search time, and increasing accessibility for users. With automated recognition of key media elements, the system aims to analyze over 36,000 minutes of audiovisual content within four months, producing thousands of object and scene detections. The platform will enable complex, multi-criteria searches, enhance metadata quality, and support future expansion into diverse media formats, thereby significantly elevating archival research and public accessibility.