Grid-Based Chip Image Analysis for Video Inspection
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Solution Overview
Problem
Current image and video analysis methods are time-consuming, costly, and prone to human error due to manual panning, zooming, and frame-by-frame analysis, which is inefficient for large datasets in applications like surveillance and geospatial analysis.
Innovation Solution
The system generates and displays multiple 'chip images' and 'video chips' simultaneously, allowing for efficient feature-based analysis by presenting panned, zoomed, or pan-and-zoomed views of images and video segments in a grid format, enabling faster and more accurate inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual panning and zooming operations are performed to inspect each object in imagery data, then detailed visual inspection of objects can be achieved, but analysis time and operational complexity increase significantly
Solution Approach 1:
The system segments the large imagery data into multiple smaller chip images, each containing a specific object or area of interest. This segmentation allows the operator to view multiple objects simultaneously in a grid layout rather than manually panning through the entire image, significantly reducing analysis time while maintaining inspection accuracy.
Solution Approach 2:
The system transitions from a single-view dimensional display to a multi-view grid display, presenting multiple chip images and video chips simultaneously in a two-dimensional grid layout. This dimensional change allows operators to compare and inspect multiple objects at once, eliminating the need for sequential panning and zooming operations.
2Measurement precision
If manual frame-by-frame video analysis is performed to detect objects and changes, then detailed inspection of video content can be achieved, but productivity and efficiency decrease
Solution Approach 1:
The system segments video streams into discrete video chips, each representing a specific time segment or detected object instance. This segmentation allows operators to inspect multiple video segments simultaneously in a grid layout, dramatically increasing analysis throughput while maintaining the ability to perform detailed frame-by-frame inspection when needed.
Solution Approach 2:
The system performs preliminary automated analysis to generate video chips with detected objects and changes already identified and organized. This preliminary action filters and prepares the video data before human inspection, allowing operators to focus on reviewing pre-processed segments rather than manually scanning entire video streams, thus improving both productivity and maintaining accuracy.
3Reliability
If traditional video comparison techniques are used to analyze multiple video streams, then content comparison can be performed, but operator fatigue increases and error rates rise
Solution Approach 1:
The system transforms the comparison task from toggling between video streams in time or side-by-side views to a grid-based spatial arrangement where multiple video chips are displayed simultaneously. This dimensional change allows operators to compare multiple video segments and detect changes across streams more easily, reducing cognitive load and fatigue while improving comparison accuracy.
4Quantity of substance
If large volumes of imagery and video data are processed manually, then comprehensive analysis can be performed, but operational costs and time requirements increase
Solution Approach 1:
The system automatically segments large volumes of imagery and video data into manageable chip images and video chips, organized in grid displays. This automated segmentation handles the voluminous data processing task, allowing operators to efficiently review organized segments rather than manually navigating through raw data, thus increasing processing volume capacity while reducing time requirements.
Solution Approach 2:
The system performs self-service automated functions including data ingestion, segmentation, object detection, and grid organization of chip images and video chips. This automation handles the labor-intensive aspects of processing large datasets, freeing operators to focus on analysis and decision-making, thereby increasing processing capacity while reducing operational time and costs.
Data Source
AI summary
Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating chip images using image data defining at least a first image and video chips using video data defining at least a first video stream. Thereafter, an array is displayed which comprises grid cells in which at least a portion of the chip images is presented, at least a portion of the video chips is presented, or a portion of the chip images and a portion of the video chips are presented. Each chip image comprises a panned-only view, a zoomed-only view, or a panned-and-zoomed view of the first image including a visual representation of at least one first object of a particular type. Each of the video chips comprises a segment of the first video stream which include a visual representation of at least one second object of the particular type.