Proximity Framing Video System Head Detection
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Solution Overview
Problem
Existing video systems struggle to provide an equitable viewing experience in conference settings, as participants far from the camera appear smaller and less focused, leading to meeting inequity.
Innovation Solution
The implementation of a video processing system that uses a head detection model to identify heads in a video stream, generates buffer bounding boxes, combines them into proximity buffer bounding boxes, and creates head frame definitions to optimize framing and focus on participants based on proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional video framing is used to capture the entire conference room, then all participants are visible in the frame, but participants far from the camera appear smaller and less focused
Solution Approach 1:
The patent segments the video stream into multiple framed views based on detected head positions and proximity relationships. Instead of a single framing for the entire room, the system creates separate framing regions that can be independently optimized, allowing participants at different distances to appear with consistent focus and size in their respective frames.
Solution Approach 2:
The patent introduces a new dimension of spatial awareness by detecting head positions and calculating proximity relationships in three-dimensional space. This enables the system to adjust framing not just based on two-dimensional image coordinates but on actual spatial relationships, creating frames that compensate for distance from the camera.
2Manufacturing precision
If the video system zooms in on individual participants to improve focus, then focus precision improves, but the coverage area decreases and other participants are excluded
Solution Approach 1:
The system divides the conference room view into multiple segmented frames based on detected head positions and proximity groups. Each segment can be independently framed and focused on specific participants or groups, allowing high focus precision for individuals while collectively maintaining coverage of all participants through the composite multi-frame view.
3Adaptability or versatility
If the video system uses a fixed framing approach, then device complexity is low, but adaptability to different participant configurations is poor
Solution Approach 1:
The patent implements dynamic framing that automatically adjusts based on real-time detection of head positions, movements, and proximity relationships. The framing parameters are continuously updated to adapt to changing participant configurations, transforming the static framing system into a dynamic one that responds to environmental changes.
Solution Approach 2:
The system uses feedback from head detection models and proximity calculations to continuously optimize framing parameters. The detected head positions and proximity relationships provide feedback that drives automatic adjustments to frame boundaries, zoom levels, and positioning, enabling adaptive framing without manual intervention.
Data Source
AI summary
A method may include obtaining, using a head detection model and for an image of a video stream, head detection information, where the head detection information identifies heads detected in the image. Method may also include obtaining buffer bounding boxes. Obtaining the buffer bounding boxes may include obtaining head buffer bounding boxes for the heads detected in the image, and combining at least two of the head buffer bounding boxes into a proximity buffer bounding box. The method may furthermore include identifying a set of templates based on the buffer bounding boxes. Method may in addition include creating, individually, head frame definitions for the buffer bounding boxes using the set of templates, generating an image frame definition that combines the head frame definitions, and processing the video stream using the image frame definition.