Surveillance System Using Dynamic Learning for Threat Detection
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Solution Overview
Problem
Conventional video surveillance systems require manual configuration and are inefficient in detecting abnormal behavior, as they rely on predefined patterns, making them time-consuming and costly to deploy, and unable to handle the vast number of possible abnormal behaviors effectively.
Innovation Solution
A surveillance system that includes a data capture module, a scoring engine module to compute abnormality and normalcy scores, and a decision-making module to generate alerts, using dynamically loaded learned data models and methods, allowing for automated detection and prediction of behavior and threat patterns in real-time across multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of detection features is used for each camera, then detection accuracy for predefined patterns is improved, but deployment time and cost increase significantly
Solution Approach 1:
The system performs self-configuration by automatically learning abnormal behavior patterns from surveillance footage without requiring manual configuration. The learning module analyzes video data and automatically generates detection features, allowing the system to configure itself rather than requiring operator intervention for each camera.
Solution Approach 2:
The system transitions from static predefined detection parameters to dynamic learned parameters. The learning module continuously adapts detection features based on actual surveillance data, changing parameters automatically to match real-world conditions rather than relying on fixed manual configurations.
2Measurement precision
If manual configuration of detection features is used for each camera, then detection accuracy for predefined patterns is improved, but deployment cost increases
Solution Approach 1:
The system performs self-configuration by automatically learning abnormal behavior patterns from surveillance footage without requiring manual configuration. The learning module analyzes video data and automatically generates detection features, allowing the system to configure itself rather than requiring operator intervention for each camera.
Solution Approach 2:
The patent replaces the mechanical process of manual configuration with an automated learning system. Instead of operators manually setting detection parameters, the system uses computational learning algorithms to automatically extract and configure detection features from surveillance data.
3Ease of operation
If predefined detection patterns are used, then system operation is simplified, but the ability to detect novel or complex abnormal behaviors is limited
Solution Approach 1:
The system transitions from static predefined detection patterns to dynamic learned patterns. The learning module continuously adapts detection capabilities based on actual surveillance data, enabling the system to detect novel and complex abnormal behaviors that were not explicitly programmed beforehand.
Solution Approach 2:
The system performs self-configuration by automatically learning abnormal behavior patterns from surveillance footage without requiring manual configuration. The learning module analyzes video data and automatically generates detection features, allowing the system to configure itself rather than requiring operator intervention for each camera.
4Adaptability or versatility
If multiple detection rules are defined to detect specific abnormal patterns, then detection coverage is improved, but system complexity and configuration effort increase
Solution Approach 1:
The system performs self-configuration by automatically learning abnormal behavior patterns from surveillance footage without requiring manual configuration. The learning module analyzes video data and automatically generates detection features, allowing the system to configure itself rather than requiring operator intervention for each camera.
Solution Approach 2:
The system transitions from static predefined detection parameters to dynamic learned parameters. The learning module continuously adapts detection features based on actual surveillance data, changing parameters automatically to match real-world conditions rather than relying on fixed manual configurations.
Data Source
AI summary
A surveillance system generally includes a data capture module that collects sensor data. A scoring engine module receives the sensor data and computes at least one of an abnormality score and a normalcy score based on the sensor data, at least one dynamically loaded learned data model, and a learned scoring method. A decision making module receives the at least one of the abnormality score and the normalcy score and generates an alert message based on the at least one of the abnormality score and the normalcy score and a learned decision making method to produce progressive behavior and threat detection.