Alarm Range Update via Multi-Dimensional Region Analysis
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Solution Overview
Problem
Video analytics systems face challenges in accurately configuring alarms to minimize false and missed alarms, as existing methods rely on representative samples and lack a precise way to adjust settings for optimal accuracy or capture rate.
Innovation Solution
A multi-dimensional optimization approach that uses user-classified alarms to update the range of characteristics, applying numerical analysis to weight true and false alarms, allowing for customizable priorities between minimizing missed and false alarms, and adjusting weights to redefine the region for improved alarm accuracy and capture rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration using representative samples is used, then system setup is simple, but alarm accuracy and capture rate cannot be optimized
Solution Approach 1:
The system automatically configures alarm parameters by analyzing historical alarm data and identifying patterns, eliminating the need for manual configuration by operators. The algorithm self-adjusts sensitivity thresholds and detection parameters based on learned patterns from past alarms, achieving both high accuracy and automatic adaptation without human intervention.
Solution Approach 2:
The system performs preliminary analysis of historical alarm data during system initialization and ongoing operation to pre-determine optimal alarm parameters. By analyzing patterns in advance and storing learned characteristics, the system prepares configuration settings before new alarm events occur, enabling rapid and accurate response without manual setup.
2Productivity
If alarm sensitivity is increased to reduce missed alarms, then capture rate improves, but false alarms increase
Solution Approach 1:
The system transitions from single-threshold alarm detection to multi-dimensional pattern analysis by examining multiple characteristics simultaneously (object size, speed, trajectory, temporal patterns). This dimensional expansion allows the system to distinguish true alarms from false ones across multiple parameters, maintaining high capture rates while filtering out false positives through comprehensive pattern matching.
Solution Approach 2:
The system dynamically adjusts alarm detection parameters based on learned patterns from historical data. By continuously modifying sensitivity thresholds, detection windows, and parameter weights according to analyzed alarm patterns, the system optimizes the balance between capture rate and false alarm rate, adapting to different operational conditions and object types.
3Reliability
If alarm sensitivity is decreased to reduce false alarms, then false alarm rate improves, but missed alarms increase
Solution Approach 1:
The system compensates for lower sensitivity thresholds by incorporating additional detection dimensions including object trajectory analysis, temporal pattern recognition, and multi-parameter validation. This multi-dimensional approach ensures that even with reduced sensitivity, the system maintains high reliability by verifying alarms across multiple characteristics before triggering false positives.
4Adaptability or versatility
If fixed alarm parameters are used, then system operation is simple, but adaptability to different applications is limited
Solution Approach 1:
The system transitions from fixed alarm parameters to dynamic, adaptive parameters that automatically adjust based on analyzed alarm patterns and operational conditions. The configuration parameters evolve over time as the system learns from historical data, enabling adaptation to different applications (real-time vs. retrospective) without manual reconfiguration or complex user intervention.
Data Source
AI summary
An object may be identified, if each measured value of a characteristic of the object is within a corresponding range of a set of characteristics. The object may then be classified as a true alarm or false alarm by a user. Next, the measured values of the object may be added as a data point to a set of data points. Each of data points is along a plurality of dimensions and each of the dimensions corresponds to one of the set of characteristics. Further, each of the data points has been classified as a true alarm or false alarm.The range of the set of characteristics may be updated to reduce a weighted score based on a number of the true alarms that are outside a region along the plurality of dimensions and a number of the false alarms inside the region for the set of data points. The region is defined based on numerical analysis of the set of data points. The weighted score may provide separate weights to the true alarms outside the region and the missed alarms inside the region.