Automatic Camera Profile Selection via Scene Classification
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Solution Overview
Problem
The manual configuration of camera profiles in video camera systems is a cumbersome process, requiring significant time and effort, especially in large security systems with hundreds of cameras.
Innovation Solution
A method and apparatus that utilize video analytics scene classification, including AI and ML models, to automatically configure camera profiles by classifying scenes and selecting appropriate profiles based on metadata, thereby automating the rule creation process and reducing installation time and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of camera profiles is used, then configuration accuracy can be ensured, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system enables self-service by allowing the camera to automatically classify scenes and select appropriate profiles without manual intervention. The camera performs self-configuration by analyzing video content, determining scene types, and autonomously applying the correct profile settings, thereby eliminating the need for manual configuration while maintaining accuracy.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting camera configuration parameters based on detected scene characteristics. Different scene types (e.g., indoor, outdoor, night, day) trigger different profile parameters such as exposure, gain, and white balance settings, allowing the camera to adapt its parameters automatically to match the current environment.
2Ease of operation
If manual configuration of camera profiles is used, then precise control over camera settings can be achieved, but the complexity of the configuration process increases
Solution Approach 1:
The camera performs self-configuration by automatically classifying scenes and selecting appropriate profiles without requiring user intervention. This eliminates the complex manual configuration process while maintaining precise control over camera settings, as the system autonomously determines and applies the correct profile based on scene analysis.
Solution Approach 2:
Instead of manually configuring the camera based on expected scene types, the system inverts the approach by having the camera automatically determine scene types from video content and then select profiles accordingly. This reversal of the configuration process simplifies operation while maintaining control precision.
3Productivity
If automatic scene classification is implemented, then configuration time is reduced, but the system complexity increases
Solution Approach 1:
The camera performs self-configuration by automatically classifying scenes and selecting appropriate profiles without manual intervention. This automation significantly reduces configuration time and accelerates deployment while the integrated nature of the scene classification and profile selection algorithms keeps the added system complexity manageable within the camera device.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium for configuring profiles for a camera, comprising receiving video from the camera. The implementations further include classifying a first scene of the first video stream. Additionally, the implementations further include determining a first metadata for the first scene. Additionally, the implementations further include selecting a first profile for the camera based on the first metadata, wherein the first profile comprises one or more configuration parameters, wherein values of each of the one or more configuration parameters of the first profile are based on the first metadata. Additionally, the implementations further include configuring the camera with the first profile.