Object Detection System Using Segmented Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for improved systems and methods to detect, identify, and track objects and events over time, particularly in educational settings and security applications, where existing technologies are inadequate for efficient object detection and event monitoring.
Innovation Solution
A system comprising an image capture device with a camera, processor, and memory, and a server with processor and memory, that captures images, performs low and high-resolution object detection and identification analysis, and stores information for timeline creation, enabling automatic attendance tracking and object/event monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution object detection and identification analysis is performed on all captured images, then measurement precision is improved, but use of energy and processing time increase significantly
Solution Approach 1:
The patent segments the object detection process into two distinct stages: low-resolution detection followed by high-resolution identification only for detected objects. This segmentation allows the system to first quickly scan all images at low resolution to identify potential objects of interest, then apply computationally intensive high-resolution analysis only to those specific regions, dramatically reducing overall energy consumption while maintaining detection precision.
Solution Approach 2:
The patent applies partial action by performing high-resolution object identification only on portions of images that contain detected objects, rather than processing entire images at high resolution. This selective approach applies computational resources precisely where needed, energy-efficiently achieving accurate object identification without the excessive energy cost of full-image high-resolution processing.
2Measurement precision
If high-resolution object detection and identification analysis is performed on all captured images, then measurement precision is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the object detection process into two distinct stages: low-resolution detection followed by high-resolution identification only for detected objects. This segmentation allows the system to first quickly scan all images at low resolution to identify potential objects of interest, then apply computationally intensive high-resolution analysis only to those specific regions, dramatically reducing overall processing time while maintaining detection precision.
Solution Approach 2:
The patent applies partial action by performing high-resolution object identification only on portions of images that contain detected objects, rather than processing entire images at high resolution. This selective approach applies computational resources precisely where needed, significantly reducing processing time while maintaining accurate object identification.
3Extent of automation
If automated image analysis is implemented for object detection and tracking, then extent of automation is improved, but device complexity increases
Solution Approach 1:
The patent segments the automated analysis system into distinct functional modules: image capture, low-resolution object detection, high-resolution object identification, and tracking. Each module performs a specific function with appropriate complexity level, allowing the system to achieve high automation while managing overall complexity through modular design and clear separation of concerns.
Data Source
AI summary
A system for detecting, identifying and tracking objects of interest over time is configured to derive object identification data from images captured from one or more image capture devices. In some embodiments of the system, the one or more image capture devices perform a first object detection and identification analysis on images captured by the one or more image capture devices. The system may then transmit the captured images to a server that performs a second object detection and identification analysis on the captures images. In various embodiments, the second analysis is more detailed than the first analysis. The system may also be configured to compile data from the one or more image capture devices and server into a timeline of object of interest detection and identification data over time.