Automatic Rule Setting for Surveillance Image Analysis
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Solution Overview
Problem
Conventional surveillance image analysis technologies require manual setting of detection rules, which is inconvenient and may lead to inaccurate analysis due to user error in drawing detection segments or regions.
Innovation Solution
An automatic rule setting method and image content analysis apparatus that analyze surveillance images to acquire scene data, determine if it conforms to predefined detection rules, and automatically set detection boundaries on target regions, reducing user involvement and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection rule setting is used, then user control over detection parameters is improved, but operation convenience and setting time are worsened
Solution Approach 1:
The system performs self-service by automatically analyzing surveillance images to extract scene data, determine detection rules, and set detection boundaries without requiring manual user input. The apparatus autonomously completes the entire rule-setting process, eliminating the need for users to manually draw detection segments or regions while maintaining accurate detection through automated scene understanding.
Solution Approach 2:
The patent replaces the mechanical manual operation of drawing detection boundaries with an automated image analysis system. The operation processor automatically processes surveillance images, extracts scene data, and generates detection rules through computational algorithms, substituting the manual mechanical drawing process with automated digital image processing and rule generation.
2Adaptability or versatility
If manual detection boundary drawing is used, then flexibility in detection region selection is improved, but measurement precision and stability are worsened
Solution Approach 1:
The system automatically determines detection boundaries by analyzing scene data from surveillance images. The operation processor autonomously identifies target regions and sets precise detection boundaries based on the extracted scene characteristics, eliminating the need for manual drawing while maintaining both flexibility and precision through automated scene understanding.
Solution Approach 2:
The system uses feedback from scene data analysis to automatically adjust and optimize detection boundaries. By continuously analyzing the surveillance image content and comparing it against detection rules, the system refines detection boundary positioning to achieve accurate results without manual intervention, ensuring both adaptability to different scenes and measurement precision.
3Productivity
If automated detection rule setting is implemented, then operation convenience and speed are improved, but device complexity is worsened
Solution Approach 1:
The operation processor is designed as a multi-functional device that integrates multiple capabilities: receiving surveillance images, analyzing scene data, determining detection rules, and setting detection boundaries. This universal processor handles all rule-setting operations through a single integrated system, avoiding the need for separate manual interfaces while managing the complexity through consolidated functionality.
Solution Approach 2:
The patent replaces complex manual rule-setting operations with automated image processing algorithms. The operation processor uses computational methods to analyze surveillance images and generate detection rules, substituting the need for complex user interfaces and manual drawing tools with streamlined automated processing that reduces operational complexity despite increased computational requirements.
Data Source
AI summary
An automatic rule setting method is applied to an image content analysis apparatus. The image content analysis apparatus includes an operation processor and an image receiver. The image receiver is adapted to receive a surveillance image. The operation processor executes the automatic rule setting method. The automatic rule setting method includes analyzing the surveillance image to acquire a scene datum, determining whether the scene datum conforms to a detection rule in accordance with a predefined condition, and automatically drawing a detection boundary of the detection rule on a target region of the surveillance image corresponding to the scene datum when the scene datum conforms to the detection rule, so as to utilize the detection boundary to acquire an object behavior parameter relevant to the detection boundary.