Video Management System Pattern Learning for False Alarm Reduction
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Solution Overview
Problem
Video management systems in security surveillance often generate false alarms due to unwanted motion, such as weather conditions, leading to unnecessary storage and user alerts for irrelevant video clips.
Innovation Solution
A method and system that analyze video images for patterns indicating movement, allowing users to categorize these patterns as true or false detections, and utilize a learning mode to save false detection patterns for future identification, thereby reducing false positives by comparing new patterns to a library of previously categorized images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video motion detection is used to trigger recordings, then motion events are detected, but false alarms increase due to unwanted motion such as weather conditions
Solution Approach 1:
The system performs preliminary pattern learning during a training phase where it analyzes motion patterns and allows users to categorize them as true positives or false positives. This preliminary classification builds a database of patterns before actual surveillance operations begin, enabling the system to make intelligent decisions about what to alert on without requiring real-time user intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where users review and classify detected motion patterns as true or false positives. This feedback loop continuously refines the pattern recognition algorithm, allowing the system to learn from user corrections and improve its accuracy over time in distinguishing between meaningful motion events and false alarms.
2Reliability
If all motion-triggered video clips are stored, then complete surveillance data is retained, but storage space is wasted on unwanted motion
Solution Approach 1:
Before full surveillance operations commence, the system performs a training phase where motion patterns are preliminarily identified and classified. During this phase, the system builds a database of motion patterns with their classifications (true positive/false positive), which then guides selective storage during actual surveillance operations.
Solution Approach 2:
The system extracts and separates motion patterns into different categories based on their characteristics. By identifying and extracting false positive patterns (such as weather conditions) from the overall motion data, the system can exclude these from storage while retaining only true positive events, thereby reducing storage requirements without compromising surveillance effectiveness.
3Reliability
If users are alerted to all motion detection events, then all potential security issues are notified, but user time is wasted on false alarms
Solution Approach 1:
The system performs preliminary pattern classification during a training phase, establishing which motion patterns should trigger alerts and which should not. This preliminary action creates a filtering mechanism that operates automatically during surveillance, so that only pre-identified true positive patterns generate user alerts, eliminating wasted time on false alarms while maintaining comprehensive security monitoring.
Solution Approach 2:
The system uses feedback from user classifications during training to refine its alerting behavior. By continuously learning from user feedback about which patterns are truly security-relevant, the system adjusts its alerting criteria to minimize false notifications, thereby reducing user time consumption while maintaining reliable security monitoring.
Data Source
AI summary
A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.