Multi-Camera Prioritization via Geometric Assessment
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Solution Overview
Problem
Manual selection of cameras in a multi-camera arrangement for surveillance systems is inefficient and time-consuming, relying heavily on operator discretion when triggered by geotagged alarms.
Innovation Solution
An assessment system that prioritizes cameras by obtaining geographical positions and properties, determining distances and expected pixel sizes of geotagged objects, comparing image data with expected pixel sizes, and assigning ratings based on conformity to automatically rank cameras for optimal object detection and capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of cameras is used, then operator discretion can be applied, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system performs self-assessment by automatically evaluating camera suitability based on geometric relationships between cameras, objects, and expected pixel sizes without requiring operator intervention. The assessment system independently determines which cameras are suitable for capturing images of geotagged objects.
Solution Approach 2:
The manual mechanical process of operator selection is replaced with an automated computational system that calculates geometric parameters, compares expected pixel sizes with actual image data, and generates suitability ratings automatically.
2Productivity
If automated assessment system is implemented, then camera selection efficiency is improved, but system complexity increases
Solution Approach 1:
The assessment system is divided into distinct functional modules: a obtaining unit for gathering camera and object data, an expectations determining unit for calculating geometric parameters and expected pixel sizes, and an assigning unit for generating suitability ratings. This segmentation makes the complex system more manageable and implementable.
3Measurement precision
If geometric comparison method is used, then camera prioritization accuracy is improved, but calculation requirements increase
Solution Approach 1:
The system calculates geometric parameters and expected pixel sizes for all cameras in the arrangement, which may be more than strictly necessary. This excessive calculation ensures that no potentially suitable camera is missed, prioritizing accuracy over minimal computational effort.
Data Source
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AI summary
The present disclosure relates to a method performed by an assessment system (1) for prioritization among cameras (21, 22, 23, 2n) of a multi-camera arrangement (2). The assessment system obtains (1001) respective geographical camera position (211, 221, 231, 2n1) and camera properties (212, 222, 232, 2n2) of each of the cameras. The assessment system further receives (1002) information data (43) indicating a geographical object position (41) and object features (42) of a physical object (4) positioned in a surrounding (5) in a potential field of view (213, 223, 233, 2n3) of each of the cameras. Moreover, the assessment system determines (1003) - for each of the cameras - by comparing the object position and object features with the respective camera position and camera properties, a respective distance (D1, D2, D3, Dn) to the object position and a respective expected pixel size of the object at the respective distance. The assessment system furthermore compares (1004) respective image data (214, 224, 234, 2n4) of the surrounding derived from each of the cameras, with the respective expected pixel size. Moreover, the assessment system assigns (1005) each of the cameras a respective rating based on to what extent the respective image data corresponds to the respective expected pixel size. The disclosure also relates to an assessment system in accordance with the foregoing, a surveillance system (3) comprising such an assessment system, and a respective corresponding computer program product and non-volatile computer readable storage medium.