Social Media Tag Extraction in Video Streams
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Solution Overview
Problem
Current systems for recognizing text in video content are inadequate in handling multiple video streams, failing to track metadata, identify social media tags, and analyze social media events, and are inefficient in handling large video data and optical character recognition processes.
Innovation Solution
A system and method for automated monitoring, extraction, and tracking of media tags in video content using a server with a recorder, detector, and OCR engine, which detects unknown text, generates metadata, and converts it into known text to identify media tags, while also monitoring social media events and providing analytics and alerts to subscribers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If a system performs optical character recognition on video content to identify text, then text detection capability is improved, but computing intensity and data processing load increase significantly
Solution Approach 1:
The system segments video content into individual frames and further segments frames into regions of interest before performing OCR. This hierarchical segmentation reduces the amount of data requiring intensive processing at any one time, thereby lowering computing intensity while maintaining text detection capability.
Solution Approach 2:
The system performs preliminary processing on video frames before OCR, including preprocessing steps like noise reduction and text region identification. This preliminary action prepares data in advance, reducing the computational burden during the actual OCR phase and improving overall processing efficiency.
2Adaptability or versatility
If a system monitors multiple video streams concurrently for text recognition, then coverage of monitored content is improved, but system complexity increases
Solution Approach 1:
The system employs a universal processing pipeline that can handle multiple video streams concurrently using the same detection and recognition algorithms. This multi-functional architecture allows the system to process any video stream through a standardized workflow, improving adaptability without proportionally increasing complexity.
Solution Approach 2:
The system creates independent processing instances for each video stream, where each instance is a copy of the core processing pipeline. This copying approach allows concurrent handling of multiple streams while reusing the same validated processing logic, thereby managing complexity through replication rather than creating entirely new processing paths for each stream.
3Loss of information
If a system collects and analyzes metadata for each detected text instance, then information completeness is improved, but data processing volume increases
Solution Approach 1:
The system extracts only the essential metadata fields relevant to social media tag analysis, such as text content, position coordinates, and timestamp. By selectively extracting only the necessary metadata rather than collecting all possible data, the system maintains information completeness for the analysis purpose while reducing overall data volume.
Solution Approach 2:
The system performs preliminary filtering and validation of metadata during the detection phase, organizing and structuring data in advance. This preliminary action reduces the need for extensive post-processing and minimizes the volume of raw data that requires further analysis, while preserving all essential information needed for social media tag identification.
Data Source
AI summary
A system for extracting and monitoring media tags within video content includes at least one server in communication with a plurality of content sources, the server receiving video content from the content sources, a recorder saving the video content, a detector receiving at least one frame of the video content, the detector detecting one or more unknown text within the frame and creating one or more images, each image associated with one of the one or more unknown text, the detector generating metadata associated with the one or more unknown text appearing in the frame, and an optical character recognition engine scanning the one or more images and converting the one or more images into one or more known text. The server further determines that the one or more known text is a media tag.