Context-Aware Object Replacement in Video Streams
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Solution Overview
Problem
Existing image and video processing technologies lack the ability to automatically and selectively remove or replace unwanted objects in real-time, leading to unintended disclosure of personal or irrelevant content during video transmissions, such as family members or background objects, which can be undesirable during remote services like music lessons or home repairs.
Innovation Solution
The system employs object recognition algorithms combined with context determination from audio, metadata, and user input to identify and replace or edit detected objects in images or videos with generic models or by blurring, ensuring that only relevant content is transmitted, using a method that includes capturing images and audio, processing them to determine context, and applying object replacement or editing based on relevance and user-defined policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image manipulation is used to remove or replace objects, then editing precision can be achieved, but productivity is reduced due to time-consuming manual processes
Solution Approach 1:
The system enables automatic object detection, classification, and replacement without requiring manual intervention. The computer automatically identifies objects in images, determines their relevance based on context, and replaces irrelevant objects with generic models or applies blurring effects, making the system self-sufficient for the editing task.
Solution Approach 2:
The patent replaces manual mechanical editing processes with automated computer vision and machine learning algorithms. Object detection algorithms automatically identify and segment objects in images, replacing the need for manual selection and editing tools that users would traditionally employ in photo editors.
2Reliability
If all detected objects are replaced to ensure privacy, then reliability of privacy protection is improved, but loss of information increases due to removal of potentially relevant content
Solution Approach 1:
The system applies different processing treatments to different objects based on their individual relevance assessments. Relevant objects are preserved in their original form, while irrelevant objects are replaced or blurred, creating a non-uniform processing approach that maintains local quality differences according to each object's importance to the context.
Solution Approach 2:
The system changes the state or visibility parameter of objects selectively based on their relevance classification. Relevant objects maintain their original visibility and detail, while irrelevant objects have their visibility reduced through replacement with generic models or application of blurring effects, thereby preserving information about important content while protecting privacy.
3Measurement precision
If context analysis is performed to determine object relevance, then accuracy of selective replacement is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system employs a multi-functional processing framework that handles object detection, audio transcription, context analysis, relevance determination, and image editing within a single integrated system. This universal approach allows the same system to perform multiple functions that would otherwise require separate tools, managing complexity through consolidation rather than proliferation of components.
Solution Approach 2:
The patent introduces an intermediary context analysis layer that bridges object detection and replacement decisions. This intermediary component analyzes audio content, transcriptions, and image context to determine object relevance, acting as a mediator that translates raw data into meaningful replacement decisions without requiring direct complex interactions between detection and editing modules.
4Productivity
If real-time processing is implemented for object replacement, then productivity is improved through faster turnaround, but use of energy increases due to continuous processing demands
Solution Approach 1:
The system processes images at discrete intervals or triggered by specific events rather than continuously analyzing every pixel change. This periodic processing approach allows real-time functionality while reducing energy consumption by activating intensive processing only when necessary, such as when new objects are detected or user interaction occurs.
Data Source
AI summary
Methods for replacing or obscuring objects detected in an image or video on the basis of image context are disclosed. Context of the image or video may be obtained via pattern recognition on audio associated with the image or video, by user-supplied context, and/or by context derived from image capture, such as the nature of an application used to capture the image. The image or video may be analyzed for object detection and recognition, and depending upon policy, the image or video context used to select objects related or unrelated to the context for replacement or obfuscation. The selected objects may then be replaced with generic objects rendered from 3D models, or blurred or otherwise obscured.