Camera Control Refinement via User Intent Learning
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Solution Overview
Problem
Existing camera control systems require skilled operator intervention and are prone to data loss or incorrect tracking, as they either focus on non-target individuals or fail to adjust for the absence of persons in the image.
Innovation Solution
An information processing apparatus that estimates control amounts for an image capturing device based on detected objects, acquires user-operated control inputs, and updates parameters to refine its estimation, using a deep neural network to learn the user's intentions and adjust camera controls accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic tracking control is performed on detected persons, then tracking automation is improved, but data loss may occur when tracking non-target persons
Solution Approach 1:
The system uses feedback from user operations (manual camera control actions) to continuously refine the automatic control algorithm. The learning unit processes the difference between automatic control amounts and actual user-operated control amounts, enabling the system to learn from user corrections and improve tracking accuracy while maintaining automation.
Solution Approach 2:
The system performs self-learning by automatically processing user operation data to update its own control parameters. The learning unit autonomously adjusts the control amount estimation based on accumulated learning data, allowing the system to improve its tracking accuracy without requiring external reconfiguration or manual intervention beyond the initial user operations.
2Extent of automation
If neural network learns image pattern and camera control relationship, then automation is improved, but incorrect control occurs when persons are absent
Solution Approach 1:
The system dynamically adjusts control parameters based on the presence and characteristics of detected persons. The estimation unit modifies control amounts according to person detection results, and the learning unit further refines these parameters through continuous learning from user operations, ensuring accurate control only when relevant targets are present.
Solution Approach 2:
The control system transitions from static pre-programmed rules to dynamic adaptive control. The control amounts are continuously adjusted based on real-time person detection results and updated learning data, allowing the system to adapt its behavior to the current scene conditions and distinguish between relevant and irrelevant persons.
3Measurement precision
If manual camera control is performed by surveillant, then control precision is improved, but operation complexity increases
Solution Approach 1:
The system introduces an intermediary learning layer between manual user operations and automatic control execution. The learning unit acts as a mediator that processes user operations and translates them into refined control parameters, allowing the system to capture the precision of manual control while reducing the operational burden on users over time as the system learns.
Data Source
AI summary
A first control amount of an image capturing device is estimated based on a region of an object detected from a captured image by the image capturing device. A second control amount of the image capturing device instructed in accordance with a user operation is acquired. A parameter required for the estimating is updated based on a difference between the first control amount and the second control amount.