Camera Imaging-Area Segmentation for Multi-Object Detection
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Solution Overview
Problem
Existing monitoring systems face challenges in efficiently and accurately detecting distributed objects using a small number of cameras, particularly when multiple types of objects are present in the field of view, leading to increased computing load and decreased detection accuracy.
Innovation Solution
An image processing device that stores table information correlating imaging area identification and detection program identification, allowing for the use of appropriate detection programs based on camera imaging areas to efficiently and accurately detect objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a small number of cameras are used to monitor distributed objects, then device complexity is reduced, but detection accuracy deteriorates when multiple types of objects are present
Solution Approach 1:
The monitoring system divides the field of view into multiple imaging areas (first imaging area and second imaging area), each assigned to detect specific object types. This segmentation allows a single camera to efficiently monitor multiple distributed objects by processing different regions with object-type-specific detection programs, thereby maintaining detection accuracy while reducing the total number of cameras required.
Solution Approach 2:
The system dynamically switches detection programs based on the imaging area being captured. The image processing device selects different detection programs corresponding to different object types according to which imaging area is currently being monitored, enabling adaptive and accurate detection of various distributed objects using a single versatile camera system.
2Measurement precision
If multiple types of detection programs are performed on captured images, then detection accuracy for different object types is improved, but computing load increases
Solution Approach 1:
The detection process is segmented by imaging area, with each imaging area associated with a specific object type and detection program. Instead of applying all detection programs to all images, the system divides the monitoring task into specialized segments, executing only the relevant detection program for each imaging area, thereby reducing overall computing load while maintaining accurate detection for each object type.
Solution Approach 2:
The system performs only the necessary detection programs for each specific imaging area rather than executing all available detection programs on every captured image. This partial action approach applies detection resources selectively and efficiently, avoiding excessive computation while ensuring accurate detection for the specific objects present in each imaging area.
3Device complexity
If different types of objects are located in the field of view of one camera, then the number of cameras is reduced, but it becomes difficult to accurately detect all objects
Solution Approach 1:
The field of view is segmented into multiple imaging areas, with each area designated for detecting specific object types. This spatial segmentation allows a single camera to effectively monitor multiple different objects by processing each imaging area with its corresponding specialized detection program, thereby maintaining high detection accuracy for all objects while using fewer cameras.
Solution Approach 2:
The single camera system is designed to perform multiple detection functions by switching between different detection programs. The image processing device acts as a universal processor that can detect various object types (first type of objects in the first imaging area, second type of objects in the second imaging area) using the same physical camera, thereby achieving multi-functionality without requiring multiple specialized cameras.
Data Source
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AI summary
An image processing device includes a storage unit configured to store table information in which at least imaging area identification information for identifying imaging areas of a camera of which an imaging area is changeable and detection program identification information for identifying detection programs for detecting a predetermined object from an image captured by the camera are correlated, and a detection unit configured to perform a process of identifying a detection program corresponding to an imaging area of the cameras using the table information stored in the storage unit and detecting the object from the image captured by the camera using the identified detection program.