Video Relevance Estimation via Action Classification Confidence
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Solution Overview
Problem
Existing methods for extracting meaning from video data are often application-specific or heuristic, leading to inefficiencies in processing vast amounts of video data, which requires automated and concise representation for efficient human review or machine analysis.
Innovation Solution
The development of systems and methods for automated video content relevance estimation, which map video data to a feature space, assign action classes, and determine relevance, using confidence scores from action classification results, applicable to both egocentric and surveillance videos without relying on clear shot boundaries or large labeled datasets, and are feature- and classifier-agnostic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prior art methods are used for extracting meaning from video data, then application-specific or heuristic approaches can be implemented, but processing efficiency deteriorates due to the vast amounts of video data requiring manual review or analysis
Solution Approach 1:
The patent extracts only the most relevant video segments based on action classification confidence scores. By identifying and isolating high-confidence action instances, the system extracts meaningful content from vast video datasets without processing every frame, thereby improving processing efficiency while reducing review time.
Solution Approach 2:
The system automatically classifies actions and estimates relevance without requiring human annotation or intervention. The action classification module and relevance estimation module work autonomously to identify meaningful video segments, enabling self-service processing that scales efficiently to large video datasets.
2Productivity
If automated relevance estimation is implemented, then processing efficiency improves, but system complexity increases due to additional modules and computational requirements
Solution Approach 1:
The patent merges the relevance estimation functionality with the existing action classification module. The relevance estimation module reuses the action classification confidence scores directly, combining multiple functions into an integrated system that reduces architectural complexity while maintaining automated processing efficiency.
Solution Approach 2:
The action classification confidence scores serve multiple purposes: they both classify the action type and estimate the relevance of video segments. This multi-functional use of the same computational output reduces the need for separate processing pipelines, thereby simplifying the overall system architecture.
3Productivity
If confidence scores from action classification are used for relevance estimation, then computational burden is minimized, but measurement precision of relevance may be insufficient
Solution Approach 1:
The system uses the action classification confidence scores as feedback to automatically determine relevance. High confidence scores indicate high relevance, creating a feedback loop where the classification result directly informs the relevance estimation without requiring additional complex computations or manual tuning.
Solution Approach 2:
The patent uses readily available action classification confidence scores as a proxy for relevance estimation, rather than investing in complex, computationally expensive relevance models. This approach uses simple, easily obtainable metrics that provide sufficient precision for practical applications while maintaining high computational efficiency.
Data Source
AI summary
A method and system for identifying content relevance comprises acquiring video data, mapping the acquired video data to a feature space to obtain a feature representation of the video data, assigning the acquired video data to at least one action class based on the feature representation of the video data, and determining a relevance of the acquired video data.