Surveillance Camera Tile Partitioning for Rapid Selection
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Solution Overview
Problem
Traditional surveillance systems face inefficiencies in locating relevant cameras due to large numbers of cameras, especially when considering field of view, leading to prolonged search times and suboptimal camera positioning in matrix views.
Innovation Solution
A surveillance system that partitions a geographic region into user-specified tiles and associates cameras with these tiles, allowing for faster and more relevant camera selection based on distance, obstructions, and camera types, while enabling user customization of matrix layouts and camera associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search and rank process is used to locate relevant cameras based on GPS location, then the system can identify cameras near a point of interest, but the search time becomes unacceptably long as the number of cameras increases
Solution Approach 1:
The geographic region is divided into multiple zones, and cameras are pre-grouped by zone. When a point of interest is selected, the system only searches within the relevant zone rather than searching through all cameras system-wide, dramatically reducing search time while maintaining location accuracy
Solution Approach 2:
Cameras are pre-associated with geographic zones before any search occurs. This preliminary organization allows the system to quickly retrieve relevant cameras by simply looking up the zone containing the point of interest, eliminating the need for time-consuming real-time searches across all cameras
2Measurement precision
If the field of view of cameras is taken into account for relevancy consideration, then the system can select more relevant cameras, but the computational task becomes even greater
Solution Approach 1:
The system determines camera relevancy by checking whether the point of interest falls within the camera's field of view, rather than performing complex computational analysis of all camera parameters. This localized check maintains high relevancy accuracy while minimizing computational complexity
Solution Approach 2:
The system replaces complex computational field of view analysis with a simpler geometric containment check - determining whether the point of interest coordinates fall within the angular boundaries of the camera's field of view. This substitution maintains accuracy while reducing computational burden
3Productivity
If a purely rank search is used to return cameras in ranked order, then the system can provide ordered results, but the layout of cameras in matrix view changes dramatically with slight GPS location differences
Solution Approach 1:
Cameras are first grouped by geographic zone, and within each zone, cameras are arranged in a consistent matrix layout. This segmentation ensures that slight variations in point of interest location within the same zone do not cause dramatic layout changes, while still allowing rapid retrieval through zone-based filtering
Solution Approach 2:
The system pre-determines the matrix layout arrangement for cameras within each zone before any user interaction. This preliminary arrangement ensures layout stability, as the same zone will always produce the same camera ordering and matrix configuration, regardless of minor point of interest location variations
Data Source
AI summary
A surveillance system includes one or more camera systems at least some of the camera systems including a camera element comprising optical components to capture and process light to produce images, camera processing circuitry that receives the light and processes the light into electrical signals and encodes the signals into a defined format, power management circuitry to power the camera system, the power management system including first and second power interfaces and first and second video output interfaces.