Real-Time Video Content Recognition via Digital Fingerprinting
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Solution Overview
Problem
Current methods fail to recognize video content in real-time while transiting on networks, such as television, which is essential for triggering specific actions immediately upon detection.
Innovation Solution
A two-step process involving conversion and real-time analysis, where unique digital fingerprints are created and stored, and then compared in real-time using separate processing units to trigger actions within 10 milliseconds, even with deteriorated video signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital watermarking or fingerprinting techniques are used to identify video content, then content identification capability is improved, but real-time recognition speed deteriorates
Solution Approach 1:
The patent divides the video content analysis into multiple independent blocks (e.g., 16x16 pixel blocks) that can be processed separately and in parallel. Each block generates its own fingerprint features, allowing the system to process different regions of video frames simultaneously, thereby improving real-time recognition speed while maintaining identification accuracy through comprehensive block-based analysis.
Solution Approach 2:
The patent transforms video content from spatial domain to frequency domain using Discrete Cosine Transform (DCT), and then to a compact fingerprint domain. This dimensional transformation reduces the complexity of real-time processing by converting complex visual information into simplified frequency coefficients that can be quickly compared, achieving both speed and accuracy in content identification.
2Reliability
If comprehensive video content analysis is performed to ensure accurate recognition, then identification reliability is improved, but processing time increases
Solution Approach 1:
The patent extracts only the most discriminative and stable features from video content for fingerprinting, such as dominant DCT coefficients and specific block patterns. By selecting and extracting only the essential features rather than analyzing all video data, the system maintains high recognition reliability while significantly reducing processing time and computational overhead.
Solution Approach 2:
The patent pre-computes and stores reference fingerprints for known video content in a database before real-time recognition is needed. During live analysis, the system only needs to compare incoming video fingerprints against these pre-prepared references, eliminating the need for time-consuming full analysis during critical real-time operations and thus reducing processing time while maintaining reliability.
3Adaptability or versatility
If digital signatures are created and stored for every video content, then content tracking capability is improved, but database complexity and storage requirements increase
Solution Approach 1:
The patent creates simplified fingerprint copies of video content characteristics rather than storing complete video files or complex metadata. Each video is represented by a compact fingerprint signature (e.g., 128-bit or 256-bit hash) that captures essential content features. This copying approach enables efficient database storage, fast comparison operations, and versatile content tracking while dramatically reducing database complexity and storage requirements compared to storing full content representations.
Data Source
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AI summary
The method involves creating unique signatures of video contents or images to-be-recognized during conversion step, where the signatures are stored in data bases. Links are created relative to actions and connected to each video content or image to start future recognition of video contents or images. A video source is analyzed in real time, and temporary signatures are created in real time and compared with the signatures in database. The actions related to the analyzed and recognized video contents are started when the signatures are recognized.