Scene Annotation and Action Description for Real-Time Media Accessibility
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Solution Overview
Problem
Existing audio-visual media, such as videogames and movies, are not accessible to individuals with disabilities, particularly those with vision impairments or color blindness, due to the lack of captioning, scene descriptions, and customizable color palettes, and adding accommodations is labor-intensive and time-consuming.
Innovation Solution
An On-Demand Accessibility System that includes modules for action description, scene annotation, color accommodation, graphical style modification, and acoustic effect annotation, utilizing neural networks to enhance accessibility by providing subtitles, text-to-speech descriptions, and customizable color palettes in real-time, without altering the original media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to add captioning and scene descriptions to audio-visual media, then accessibility for disabled persons is improved, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical processes of captioning and scene description with automated machine learning systems. Neural networks analyze video content, generate scene annotations, and create captions automatically, eliminating the need for human operators to manually transcribe and describe each scene, thus resolving the contradiction between improving accessibility and reducing time consumption
Solution Approach 2:
The system enables the audio-visual media itself to generate accessibility features through automated analysis. The machine learning model processes the video content directly to extract scenes, objects, and actions, then generates appropriate captions and descriptions without requiring external human intervention, allowing the system to serve itself in creating accessibility accommodations
2Adaptability or versatility
If comprehensive scene descriptions and color customizations are added to accommodate all disabled users, then accessibility is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the video content into discrete scenes and identifies individual objects within each scene. By breaking down the complex task of describing entire video sequences into smaller, manageable scene units, the system can process and generate descriptions more efficiently, reducing overall system complexity while maintaining comprehensive accessibility coverage
Solution Approach 2:
The system dynamically adjusts color palettes and scene descriptions based on user needs and specific scene content. Rather than applying fixed, complex transformations to all content, the system adapts its processing in real-time based on the detected objects, actions, and visual characteristics of each scene, reducing unnecessary processing complexity while maintaining high adaptability
3Productivity
If automated machine learning systems are used to generate accessibility features, then processing speed is improved, but accuracy and reliability of descriptions may be compromised
Solution Approach 1:
The system performs preliminary training and validation of the machine learning models using labeled datasets before deployment. By pre-training the neural networks with ground truth data and validating their performance, the system ensures high accuracy in scene recognition, object detection, and caption generation, maintaining reliability while achieving fast processing speeds during actual use
Solution Approach 2:
The system incorporates feedback mechanisms where generated captions and scene descriptions are evaluated against ground truth data during training, and where user corrections can be fed back to improve model performance. This continuous feedback loop ensures that the automated system maintains high accuracy and reliability in its descriptions while operating at automated processing speeds
Data Source
AI summary
A system enhances existing audio-visual content with an action a scene annotation module, an action description module, both of which are coupled to a controller. The scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements. The scene annotation module includes a first neural network configured to generate a feature vector from the image frame and a second neural network configured to generate a caption describing elements within the image frame from the feature vector. The action description module recognizes action happening within one or more image frames received from the host system and generates a description of the action happening within one or more image frames.