Webcam Auto-Framing via AI Pointing Element Detection
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Solution Overview
Problem
Current information handling systems, such as notebooks and laptops, lack a convenient method to automatically focus or auto-frame objects captured by webcams, requiring manual adjustments and lacking solutions for maintaining the entire field of view.
Innovation Solution
A computer-implementable method using an auto-framing AI/ML model that captures a full field of view image by a webcam, detects a region of interest (ROI) and a pointing element, and auto-frames the object of interest to which the pointing element is directed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual adjustment of webcam distance or PTZ is used to achieve close-up capture, then the object can be framed, but manual intervention is required and the entire field of view cannot be maintained
Solution Approach 1:
The system enables self-service by automatically detecting objects and framing them without requiring manual user intervention. The AI/ML model processes webcam images to identify objects and automatically adjusts the camera framing, allowing the system to serve itself rather than requiring continuous manual adjustment.
Solution Approach 2:
The patent replaces manual mechanical adjustment (physical movement of webcam or PTZ controls) with an automated computational system. Instead of mechanically adjusting the camera position or angle, the system uses AI/ML algorithms to detect objects and automatically controls the framing, substituting mechanical operations with intelligent automation.
2Ease of operation
If PTZ adjustment is used to achieve close-up capture, then the object can be focused, but manual intervention is required
Solution Approach 1:
The system performs self-service by automatically detecting objects and executing framing actions without user intervention. The AI/ML model processes images, identifies objects of interest, and automatically controls the camera framing, eliminating the need for users to manually adjust PTZ or other camera settings.
Solution Approach 2:
The system implements feedback mechanisms where the AI/ML model continuously processes webcam images, detects objects, and adjusts framing based on real-time visual feedback. This closed-loop system monitors the scene and automatically modifies camera positioning to maintain optimal framing of detected objects.
3Measurement precision
If the webcam focuses on an object, then close-up capture is achieved, but the entire field of view is lost
Solution Approach 1:
The system applies segmentation by dividing the field of view into different regions of interest. The AI/ML model identifies specific objects within the broader scene and creates focused framing around those objects while maintaining awareness of the overall context. This allows the system to segment attention between detailed object view and broader field of view.
Solution Approach 2:
The system dynamically adjusts the field of view based on detected objects. Rather than maintaining a fixed framing, the camera dynamically modifies its field of view to focus on objects of interest while preserving the ability to show broader context. This dynamic adjustment allows the system to adapt between close-up and wide-view modes as needed.
Data Source
AI summary
Described are methods and systems for auto framing an object of interest. A webcam of an information handling system captures a full field of view image that has a region of interest (ROI) that includes a pointing element, such as an active pen, user finger, or other such object. The ROI also includes the object of interest. An auto-framing AI/ML model detects the ROI and the pointing element and auto-frames the object of interest to which the pointing element is directed to.