Crowd Anomaly Detection Using Time-Averaged Histogram Modeling
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Solution Overview
Problem
Conventional anomaly detection methods in automated security systems are unreliable due to high data correlation and require strict, user-defined criteria, leading to false positives or negatives in crowd size and growth detection.
Innovation Solution
A video security system using an image sensor and electronic processor that decorrelates crowd data by averaging over time intervals, updates histograms, and fits them to decay functions to accurately detect anomalous crowd sizes and growth, generating alerts based on probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anomaly detection methods use strict user-defined criteria for crowd size detection, then the detection rules are clear and enforceable, but false positives and false negatives increase due to high data correlation and inability to adapt to varying contexts
Solution Approach 1:
The system dynamically changes detection parameters by adapting crowd size thresholds and growth rate criteria based on historical data and contextual information. Instead of using fixed user-defined criteria, the system modifies detection parameters automatically to match varying contexts such as time of day, location, and event types, thereby reducing false positives and negatives while maintaining reliable anomaly detection
Solution Approach 2:
The system performs self-service by automatically learning from historical crowd data and adjusting its detection criteria without requiring continuous user intervention. The anomaly detection system serves itself by autonomously updating its understanding of normal crowd patterns and adapting its thresholds, which reduces the complexity of manual configuration while improving reliability through data-driven adaptations
2Measurement precision
If the system averages crowd data over time intervals to reduce data correlation, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial averaging by selectively averaging crowd data over optimized time intervals rather than processing all possible time combinations. By determining appropriate averaging windows based on crowd dynamics and detection requirements, the system achieves sufficient correlation reduction and measurement precision without the excessive computational burden of complete temporal averaging
Solution Approach 2:
The system segments the continuous crowd data stream into discrete time intervals for selective averaging. By dividing the data processing into manageable segments with specific averaging applied to each, the system reduces overall computational complexity while maintaining precision in crowd size measurements through targeted correlation reduction in each segment
3Reliability
If the system updates histograms for each non-overlapping time interval and fits decay functions, then anomaly detection accuracy improves, but computational complexity and processing load increase
Solution Approach 1:
The system applies partial histogram updating by selectively updating histograms only for significant crowd changes rather than updating for every time interval. By fitting decay functions only when necessary to model specific crowd patterns, the system maintains high anomaly detection reliability through targeted analysis while reducing overall computational processing power requirements compared to continuous full updates
4Measurement precision
If the system uses probability thresholds based on historical data to generate alerts, then false alarms are minimized, but the system requires extensive historical data collection and storage
Solution Approach 1:
The system extracts only the essential statistical features and patterns from historical crowd data needed for probability threshold calculation, rather than storing and processing all raw historical data. By extracting key characteristics such as average crowd sizes, variance patterns, and temporal dependencies, the system achieves precise anomaly detection with reduced data storage requirements
Solution Approach 2:
The system creates simplified probabilistic models that copy the essential behavior patterns from historical data without requiring the full historical dataset. By generating compact probability distributions and threshold values that represent historical crowd patterns, the system maintains high detection precision while minimizing the quantity of stored historical data
Data Source
AI summary
Examples provide a video security system including an electronic processor configured to detect an anomaly by obtaining video data captured using an image sensor and determining a number of people detected in each of a plurality of frames of the video data. For each moment t, the electronic processor determines an average number of people n over a time interval (t-Δt1, t), where Δt1 is greater than or equal to the moment t. For each non-overlapping time interval Δt1, the electronic processor updates a histogram based on the average number of people for the time interval Δt1. In response to determining, using the histogram, that the probability ρ of the average number of people n over the time interval (t-Δt1, t) is less than a threshold probability ρ*, the electronic processor generates an alert indicating that an anomalous crowd size is detected.