Integrated Anomaly Detection Using Neural Network Tracking
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Solution Overview
Problem
Current anomaly detection systems in CCTV environments struggle to comprehensively detect multiple anomalous situations in real-time, are prone to false human detection and tracking failures, and lack versatility in unlearned environments due to limited data collection and environmental adaptability.
Innovation Solution
An integrated anomaly detection method using neural networks for object and human detection, tracking, and situation analysis, which combines spatial and texture information to generate feature vectors for robust human tracking and anomaly identification, and employs image-text comparison for detecting situations like arson without additional data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anomaly detection systems use single-purpose detection methods optimized for specific situations, then detection accuracy for that specific situation is improved, but the system cannot comprehensively detect multiple different anomalous situations
Solution Approach 1:
The patent implements a unified anomaly detection framework that integrates multiple detection modules (intrusion detection, loitering detection, abandonment detection, arson detection, falling detection, fighting detection) into a single system. This multi-functional approach allows the system to detect various anomalous situations comprehensively while maintaining detection accuracy through specialized sub-modules for each anomaly type.
Solution Approach 2:
The detection system is divided into separate functional modules, each optimized for detecting specific anomaly types. The framework segments the overall detection task into independent detection units that can operate simultaneously, allowing each module to maintain high detection accuracy for its specific function while contributing to comprehensive multi-anomaly detection capability.
2Measurement precision
If human detection and tracking technology is used in CCTV environments, then human object detection capability is improved, but false detection and tracking failures occur due to lighting changes, weather, and environmental variations
Solution Approach 1:
The system employs feedback mechanisms where detection results are continuously refined based on tracking information, and tracking is adjusted based on detection outcomes. This closed-loop approach allows the system to correct false detections and tracking errors by comparing sequential frames and adjusting detection parameters dynamically, improving both accuracy and reliability in varying environmental conditions.
Solution Approach 2:
The detection and tracking system dynamically adapts to changing environmental conditions by adjusting detection parameters and tracking algorithms in real-time. The system modifies its behavior based on lighting changes, weather conditions, and scene variations, maintaining stable tracking performance through dynamic parameter adjustment rather than fixed thresholds.
3Measurement precision
If anomaly detection systems are trained using previously collected data, then detection performance on trained scenarios is improved, but the system lacks versatility in unlearned, new CCTV environments without additional data collection
Solution Approach 1:
The system performs preliminary anomaly detection using general-purpose detection modules before environment-specific training data is available. The framework enables initial operation with broad detection capabilities that can function in new environments immediately, with the option to refine performance later through data collection. This allows the system to be deployed versatility-first, then optimized for specific environments over time.
Data Source
AI summary
Disclosed herein is a method for integrated anomaly detection. The method includes detecting a thing object and a human object in input video using a first neural network, and tracking the human object, and detecting an anomalous situation based on an object detection result and a human object tracking result.