Unauthorized Subject Detection Through Adaptive Event Similarity
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Solution Overview
Problem
Conventional video analytic technologies struggle with inconsistent accuracy under varying environmental conditions, leading to unnecessary alerts in unauthorized subject detection, as they fail to reliably determine if an event relates to an authorized or unauthorized subject.
Innovation Solution
A method and apparatus that determine the likelihood of an event's similarity to authorized or unauthorized subject events based on data matching, using matching thresholds to assign appropriate identifiers and minimize false alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional face recognition technology is used to determine subject authorization, then the system can quickly identify authorized subjects, but the accuracy decreases under varying environmental conditions leading to false alerts
Solution Approach 1:
The system dynamically adjusts the matching threshold based on environmental conditions and confidence scores. When environmental conditions vary (lighting, angle, quality), the system adapts by lowering the threshold to prevent false negatives, while maintaining high confidence requirements to avoid false positives. This dynamic adjustment resolves the contradiction between maintaining high accuracy and adapting to varying conditions.
Solution Approach 2:
The system changes the parameter of matching threshold dynamically based on environmental assessment. In optimal conditions, a high threshold ensures high precision. In challenging conditions, the threshold is adjusted to maintain detection capability while using multiple verification checks to preserve accuracy. This parameter change strategy resolves the contradiction between reliability and adaptability.
2Speed
If the system generates an alert immediately upon detecting a possible unauthorized subject, then response time is fast, but unnecessary alerts are generated due to inaccurate detection
Solution Approach 1:
The system performs preliminary verification by checking multiple events and patterns before generating an alert. Instead of immediate alerting, the system first assesses whether the detected subject matches known unauthorized patterns, checks environmental conditions, and verifies consistency across multiple detections. This preliminary action reduces false alerts while maintaining fast response for genuine threats.
Solution Approach 2:
The system uses feedback from multiple event detections and confidence scores to determine whether to generate an alert. By analyzing patterns across multiple events and using confidence thresholds, the system provides feedback to the alert generation process, ensuring that only high-confidence unauthorized subject detections trigger alerts. This feedback mechanism resolves the contradiction between speed and reliability.
3Reliability
If the system uses strict matching criteria to ensure high accuracy, then false alerts are minimized, but the system fails to detect unauthorized subjects under varying conditions
Solution Approach 1:
The matching criteria are made dynamic rather than static. The system adjusts the strictness of matching based on environmental conditions, confidence scores from multiple events, and pattern recognition results. In optimal conditions, strict criteria reduce false alerts. In challenging conditions, the system relaxes criteria while using multiple verification layers to maintain reliability, thus resolving the contradiction between reducing false alerts and maintaining detection capability.
Data Source
AI summary
Present disclosure provides a method for determining if an event relates to an unauthorized subject, the method comprising: determining a likelihood of how the event is similar to at least one of: (i) at least one event of a list of events relating to the unauthorized subject and (ii) at least one of a list of events relating to an authorized subject, each event of the list of events comprising data identifying the unauthorized subject, the determination of likelihood being based on the data identifying the unauthorized subject; and determining the event to relate to the unauthorized subject in response to the determination of the likelihood.