Lexical Analyzer for Neuro-Linguistic Behavior Recognition
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Solution Overview
Problem
Current surveillance and monitoring systems, such as video surveillance and SCADA systems, are rigid and require predefined rules to detect behaviors, leading to missed alerts and high resource consumption, making them difficult to scale effectively.
Innovation Solution
A neuro-linguistic behavior recognition system builds a dictionary of words from input data using a hierarchical learning model, identifying statistically significant combinations of symbols to recognize patterns without predefined rules, thereby distinguishing normal from abnormal activity and reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based surveillance systems are used to detect specific activities, then the system can alert administrators for predefined behaviors, but the system is rigid and cannot detect behaviors that do not conform to predefined rules
Solution Approach 1:
The system performs self-training by automatically learning from video data to build behavior models without requiring manual rule definition. The neural network autonomously identifies patterns and creates detection rules, enabling the system to adapt to new behaviors automatically while maintaining reliable detection of both predefined and emerging behavior patterns
Solution Approach 2:
The system transforms rigid rule-based parameters into adaptive learned parameters through neural network training. By changing from fixed threshold rules to dynamic, data-driven parameters, the system achieves both reliability in detection and adaptability to diverse behaviors including those not initially predefined
2Adaptability or versatility
If the system trains itself to identify behaviors, then it can recognize patterns without hard-coded rules, but it still requires rules to be defined in advance for what to identify
Solution Approach 1:
The system completely eliminates the need for manual rule definition by implementing full self-training capability. The neural network automatically learns what behaviors to identify from raw video data, performing both pattern recognition and rule generation autonomously, thereby achieving high adaptability while reducing device complexity related to rule management
3Reliability
If video surveillance systems process large amounts of video data, then they can monitor activities comprehensively, but they require significant computing resources including processor power, storage, and bandwidth
Solution Approach 1:
The system extracts only the essential behavioral features from video data using neural network learning, rather than processing entire video streams. By taking out and focusing on key motion patterns and behavior characteristics, the system maintains comprehensive monitoring coverage while dramatically reducing computing resource consumption for processing, storage, and bandwidth requirements
Solution Approach 2:
The system changes from processing raw video pixels to processing extracted behavioral parameters and features. This parameter transformation from high-dimensional video data to low-dimensional behavior representations maintains reliable monitoring of all activities while reducing computational energy consumption by orders of magnitude
4Reliability
If typical video surveillance systems process camera feeds, then they can provide comprehensive surveillance, but they are difficult to scale due to high resource costs
Solution Approach 1:
The system extracts behavioral essence from video data, processing only necessary features rather than complete camera feeds. This extraction approach maintains reliable surveillance coverage across multiple cameras while enabling scalable deployment by reducing the computational burden per camera feed, thereby improving overall system productivity and ease of scaling
Solution Approach 2:
The neural network model serves multiple functions simultaneously: it performs behavior recognition, pattern classification, and anomaly detection across diverse video sources. This multi-functionality allows a single scalable architecture to handle various surveillance scenarios without requiring separate systems, thereby improving both surveillance coverage reliability and system scalability
Data Source
AI summary
Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.