Auto-Framing Camera System for Dynamic Conference Object Detection
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Solution Overview
Problem
Conventional image processing systems for video conferences face challenges in detecting objects, especially when participants are not facing the camera or are in varying positions, leading to incorrect framing and increased user burden, as they require participants to remain still and focused on the camera without adapting to different scenarios like standing or mixed seating arrangements.
Innovation Solution
An image processing apparatus that generates multiple images of a scene, detects objects using face detection methods like neural networks, and calculates an object existing probability distribution to determine a stable photographing range, allowing for automatic framing adjustments, including pan-tilt control, to ensure all objects are captured accurately and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If face detection is used to detect participants, then object detection can be performed, but detection reliability deteriorates when participants are not facing the camera or are in dynamic positions
Solution Approach 1:
The system dynamically adjusts the photographing range based on detected object positions. When objects are detected outside the current frame, the camera automatically pans and tilts to include them, creating a dynamic adaptation to varying participant positions and orientations throughout the conference.
Solution Approach 2:
The system continuously monitors object detection results and uses this feedback to adjust the photographing range. The detected object positions serve as feedback signals that trigger automatic camera adjustments, creating a closed-loop system that adapts to participant behavior in real-time.
2Adaptability or versatility
If auto-framing is performed continuously, then all objects can be captured, but device complexity increases due to constant pan-tilt control
Solution Approach 1:
The system performs preliminary actions by detecting objects in advance and proactively adjusting the photographing range before objects move out of the frame. This predictive approach reduces the need for continuous reactive adjustments, simplifying the control system while maintaining comprehensive coverage.
Solution Approach 2:
The pan-tilt control operates dynamically only when necessary, adjusting the photographing range based on detected object positions. The system transitions between static and dynamic modes, maintaining simplicity during stable conditions while providing adaptability when objects move or appear outside the current frame.
3Measurement precision
If participants must remain still and facing the camera, then detection accuracy improves, but ease of operation deteriorates as users bear the burden of maintaining fixed positions
Solution Approach 1:
The system performs self-service by automatically detecting objects and adjusting the photographing range without requiring user intervention. The camera autonomously tracks and frames all participants, eliminating the burden on users to maintain fixed positions or orientations while preserving detection accuracy.
Solution Approach 2:
Instead of requiring participants to adapt to the camera's fixed framing, the system inverts the approach by having the camera adapt to the participants' positions. This inversion transfers the adjustment burden from users to the automated system, significantly improving ease of operation.
4Device complexity
If conventional face detection is used, then simple detection can be performed, but measurement precision deteriorates when objects move or are in unexpected positions
Solution Approach 1:
The system uses feedback from object detection to continuously refine and adjust the photographing range. When objects move or appear in unexpected positions, the detection results provide feedback that triggers automatic camera adjustments, maintaining high measurement precision without requiring complex detection algorithms.
Data Source
AI summary
An image processing apparatus includes a photographing unit configured to generate a plurality of images by photographing a plurality of times a range which can be photographed, an object detection unit configured to detect a specified object from each of the plurality of images generated by the photographing unit, a position determination unit configured to determine an existing position of the specified object based on a detection result of the specified object in the plurality of images, and a range determination unit configured to determine a photographing range to be photographed within the range which can be photographed based on the existing position of the specified object determined by the position determination unit.