Auto-Setting Video Modules Using Score Functions
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Solution Overview
Problem
Large-scale video surveillance systems face challenges in optimizing image quality and resource management due to suboptimal camera settings and inefficient resource allocation, leading to issues like motion blur, exposure problems, and increased costs associated with complex management and maintenance.
Innovation Solution
A method and system for auto-setting image acquisition and processing modules in video systems, which involves scoring functions to optimize camera settings and resource allocation based on efficiency and resource consumption, allowing for dynamic adaptation to environmental changes and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration and monitoring is used for camera settings and resource allocation, then system adaptability to environmental changes is limited, but system complexity and automation level remain low
Solution Approach 1:
The system implements self-service through automatic camera setting optimization and dynamic resource allocation. The optimization module automatically adjusts camera parameters based on environmental conditions and task requirements, while the resource allocation module dynamically assigns resources without manual intervention, enabling the system to adapt to changes autonomously
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring camera performance metrics and resource consumption levels. The optimization module receives feedback on image quality and automatically adjusts settings, while the resource allocation module monitors resource usage and reallocates dynamically to maintain optimal system performance under varying conditions
2Reliability
If redundancy and overcapacity are used to handle resource consumption peaks, then resource availability during peaks is improved, but system costs and resource waste increase
Solution Approach 1:
The system applies dynamics by implementing dynamic resource allocation that adapts to real-time resource consumption patterns. The resource allocation module continuously monitors actual usage and adjusts resource assignment dynamically, allowing the system to handle peaks efficiently without permanent overcapacity infrastructure
Solution Approach 2:
The system utilizes parameter changes by adjusting resource allocation parameters based on monitored consumption patterns. The resource allocation module modifies allocation parameters dynamically in response to changing system conditions, enabling flexible resource management that prevents waste while ensuring availability during peak demand
3Productivity
If the number of cameras and processing modules is increased to improve coverage and image quality, then system performance and image quality improve, but resource consumption and management complexity increase
Solution Approach 1:
The system implements universality by enabling processing modules to handle multiple camera inputs and diverse processing tasks. The resource allocation module dynamically assigns processing resources to different cameras based on current needs, allowing the same hardware resources to serve multiple functions and reducing overall resource consumption
Solution Approach 2:
The system applies merging by consolidating processing resources that can be shared across multiple cameras. The resource allocation module combines processing capabilities into unified resource pools that can be dynamically allocated, reducing redundant hardware and lowering overall resource consumption while maintaining system performance
4Manufacturing precision
If optimal camera settings are manually configured for each camera, then image quality is improved, but system setup time and operational complexity increase
Solution Approach 1:
The system implements self-service through automatic camera setting optimization where the optimization module autonomously determines and applies optimal camera parameters based on environmental conditions and task requirements, eliminating the need for manual configuration while maintaining high image quality
Solution Approach 2:
The system utilizes parameter changes by automatically adjusting camera parameters based on monitored environmental conditions and performance metrics. The optimization module dynamically modifies camera settings parameters to maintain optimal image quality without requiring manual intervention or complex setup procedures
Data Source
AI summary
At least one embodiment of a method of setting a module of a set of image acquisition and processing modules in a video system, the modules sharing one same resource, the method comprising:obtaining, for each module, a score function depending on an efficiency value to perform a task to which the module is assigned, a level of consumption of the resource, and a trade-off value characterizing the importance of the efficiency of performing a task relative to resource consumption,determining an updated value of the trade-off value of the resource, so that the resource consumption level reaches a threshold;identifying settings of the module optimizing a result of the score function of the module when considering the updated trade-off value; andsetting the module according to the identified settings.