Gaze-Guided Object Correlation for Video Surveillance
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Solution Overview
Problem
Existing automated security and surveillance systems rely heavily on human observation for object detection and classification, lacking efficient computer-implemented analysis to identify similarities and differences between objects in video footage.
Innovation Solution
A method and system that utilize gaze determination to identify user focus on objects within images, calculating similarity scores between objects and backgrounds, and initiating analytics-based actions when thresholds are exceeded or fallen below, enabling automated object classification and notification of similarities or differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observers manually detect and classify visual objects, then classification accuracy can be maintained, but system productivity and response time deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising image processing units and similarity analysis units that automatically compare objects across images. This intermediary automated system processes visual data to identify similarities and differences between objects, enabling the system to maintain high classification accuracy while significantly improving productivity by eliminating manual observation bottlenecks.
Solution Approach 2:
The patent replaces the mechanical human observation process with an automated computer-based system. The image processing units and similarity analysis units perform object detection, extraction, and comparison operations automatically, substituting human cognitive processing with computational algorithms that operate at machine speed while maintaining analytical accuracy.
2Productivity
If automated object detection is implemented, then productivity improves, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent segments the automated detection process into distinct functional units: image processing units that extract object characteristics, similarity analysis units that compare objects across images, and notification units that alert operators of significant differences. This segmentation allows each unit to specialize in specific tasks, improving overall system throughput while maintaining precision through focused processing at each stage.
Solution Approach 2:
The patent employs parameter changes by adjusting similarity thresholds and comparison criteria dynamically. The system modifies detection sensitivity parameters based on operational requirements, enabling high throughput by optimizing the balance between processing speed and detection precision through adaptive parameter adjustment rather than fixed thresholds.
3Measurement precision
If comprehensive object comparison is performed, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent extracts and isolates specific object characteristics and key visual features for comparison, rather than processing entire images comprehensively. By extracting only the essential object attributes (such as appearance, position, and distinguishing features) and focusing similarity analysis on these extracted elements, the system achieves high identification accuracy while reducing computational complexity and system architecture burden.
Data Source
AI summary
A method, system and computer program product for correlating objects of interest based on difference and similarities analysis is disclosed. The method includes receiving input that a human user is focused on a portion of a first image during a first period of time, and also focused on a portion of a second image during a second period of time. In response to the input being received, a first analytics-based action may be initiated when certain similarity/difference conditions are met, or a second analytics-based action may be initiated when certain other similarity/difference conditions are met.