VCA Reference Results for Auto-Setting Video Surveillance
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Solution Overview
Problem
Current video surveillance systems face limitations in real-time responsiveness and efficiency due to the need for manual setting adjustments by specialized installers, which is costly and time-consuming, especially when dealing with complex tasks like human tracking and abnormal behavior detection.
Innovation Solution
A method for generating video content analytics (VCA) reference results that can be used to automatically set VCA system configurations, involving video data analysis, filtering, and validation to determine optimal settings, allowing for efficient and cost-effective deployment without requiring specialized installers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting adjustments by specialized installers are used, then VCA system settings can be finely tuned, but deployment cost and time increase
Solution Approach 1:
The system performs automatic scene classification and VCA setting optimization without requiring specialized installers. The VCA system analyzes video data, classifies scenes, and determines optimal settings autonomously, enabling self-service deployment that reduces both time and cost while maintaining setting quality
Solution Approach 2:
The system pre-classifies scenes and pre-determines optimal VCA settings based on scene characteristics before actual deployment. By performing scene classification and setting optimization in advance through automated analysis, the system prepares configuration data that can be quickly applied, reducing on-site deployment time
2Measurement precision
If specialized installers are used for VCA setting, then setting quality improves, but operational cost increases
Solution Approach 1:
The VCA system automatically performs scene classification and setting optimization without human intervention. The automated process analyzes video content, determines scene types, and configures VCA parameters independently, eliminating the need to pay specialized installers while maintaining setting quality
Solution Approach 2:
The system replaces the manual mechanical process of installer configuration with an automated computational process. Scene classification algorithms and automated setting optimization replace the physical presence and manual work of specialized installers, reducing operational costs while preserving setting quality
3Device complexity
If fixed VCA settings are used, then system complexity is reduced, but adaptability to different scenes deteriorates
Solution Approach 1:
The system dynamically adapts VCA settings based on real-time scene classification. Instead of using fixed settings, the system continuously analyzes video data, identifies scene characteristics, and adjusts VCA parameters accordingly, enabling the system to adapt to different scenes automatically while maintaining manageable complexity through algorithmic control
Solution Approach 2:
The system changes VCA parameters based on detected scene characteristics. By automatically adjusting settings such as detection thresholds, analysis depth, and processing intensity according to scene type (e.g., indoor vs. outdoor, day vs. night), the system achieves high adaptability without requiring complex manual reconfiguration
4Use of energy by moving object
If automatic auto-setting process is implemented, then deployment cost decreases, but setting reliability may worsen
Solution Approach 1:
The system implements feedback mechanisms where VCA performance is continuously monitored and evaluated. Scene classification results and setting effectiveness are fed back into the system, allowing automatic refinement and optimization of settings over time, thereby ensuring reliability without requiring expensive manual intervention
Solution Approach 2:
The system performs preliminary scene classification and setting optimization using trained algorithms before deployment. By pre-processing video data and determining optimal settings in advance through automated analysis, the system establishes reliable configurations that have been validated through computational evaluation, ensuring quality without manual involvement
Data Source
AI summary
At least one embodiment of a method of controlling a video surveillance system, the video surveillance system comprising a video content analytics module and a video source, comprises:obtaining, by the video content analytics module, from the video source, video data comprising sequences frames, content of the video data comprising a representation of at least one object;analyzing at least a part of the video data in the video content analytics module to obtain video content analytics results;filtering the obtained video content analytics results to validate at least a subset of the video content analytics results, the validated video content analytics results forming a set of video content analytics reference results; anddetermining a value for at least one configuration parameter of the video surveillance system based on at least a portion of the video content analytics reference results.