Video Indexing via Visual Cue Emotion Detection
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Solution Overview
Problem
Existing video indexing systems fail to capture personalized viewer reactions, relying on generalized analysis that may not accurately represent individual emotional responses, and often require user feedback that can be prone to errors and detract from the viewing experience.
Innovation Solution
A system that estimates viewer emotional reactions based on detected visual cues, indexing videos with metadata about emotions and their timing, allowing for personalized video summarization, partitioning, and recommendation, and leveraging crowd-sourced emotional responses for enhanced search and recommendation algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single generalized video analysis result is provided, then the system complexity is reduced and processing is simplified, but the accuracy of representing individual viewer emotional responses deteriorates
Solution Approach 1:
The patent segments the video stream into multiple segments and requests continuous user feedback for each segment rather than providing a single generalized analysis. This segmentation allows the system to capture nuanced emotional responses at different points in the video, improving measurement precision while maintaining manageable system complexity through automated processing of segmented feedback.
Solution Approach 2:
The system dynamically adapts the feedback request process by continuously sampling user responses throughout the video stream rather than relying on a static single-rating approach. This dynamic feedback collection enables the system to track emotional responses over time, improving accuracy without significantly increasing overall system complexity.
2Measurement precision
If continuous sampling of user responses is requested throughout the video stream, then the accuracy of viewer reaction capture is improved, but the ease of operation deteriorates due to user burden
Solution Approach 1:
The system employs automated visual cue detection technology that passively monitors user emotional states through facial expressions and body language without requiring active user participation. This self-service approach captures continuous feedback throughout the video stream while freeing users from the burden of manual rating, thereby improving measurement precision while maintaining ease of operation.
Solution Approach 2:
The patent replaces the mechanical interaction of manual user rating with an automated optical system that detects visual cues from the user's face and body. This substitution eliminates the need for users to physically interact with the feedback mechanism, reducing operational burden while enabling continuous, accurate capture of emotional responses throughout the video viewing experience.
3Ease of operation
If automated visual cue detection is implemented, then the need for user feedback effort is reduced, but the device complexity increases due to additional detection and analysis systems
Solution Approach 1:
The system integrates multiple functions into a unified automated feedback platform that combines visual cue detection, emotional state analysis, and video segment correlation. By making the system universal and multi-functional, it reduces the need for separate dedicated systems for each task, thereby managing device complexity while providing comprehensive automated feedback collection that minimizes user effort.
4Adaptability or versatility
If user feedback systems are used, then personalized viewer experience can be captured, but errors increase due to user mistakes and misunderstanding of rating systems
Solution Approach 1:
The patent replaces manual user rating mechanisms with automated visual cue detection systems that objectively measure emotional states through facial expressions and body language. This substitution eliminates errors caused by user mistakes, misinterpretation of rating scales, and inconsistent feedback provision, thereby improving reliability while maintaining the ability to deliver personalized viewer experiences based on accurately captured emotional data.
Data Source
AI summary
Generally, this disclosure provides methods and systems for video indexing systems with viewer reaction estimation based on visual cue detection. The method may include detecting visual cues generated by a user, the visual cues generated in response to the user viewing the video; mapping the visual cues to an emotion space associated with the user; estimating emotion events of the user based on the mapping; and indexing the video with metadata, the metadata comprising the estimated emotion events and timing data associated with the estimated emotion events. The method may further include summarization, partitioning and searching of videos based on the video index.