Neuro-linguistic Mapper for Behavior Recognition
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Solution Overview
Problem
Current surveillance and monitoring systems require predefined knowledge of behaviors to recognize activities, and they consume significant computing resources, making them difficult to scale effectively.
Innovation Solution
A neuro-linguistic behavior recognition system generates symbols from normalized input data vectors, clustering and mapping feature values to create a linguistic model that distinguishes between normal and abnormal activities without predefined patterns, utilizing neural networks and adaptive resonance theory to learn and recognize patterns over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional surveillance systems use predefined patterns and hard-coded behaviors for activity recognition, then the system can reliably detect specific activities, but the system requires significant computing resources and is difficult to scale
Solution Approach 1:
The patent transforms video data from raw pixel values to normalized feature vectors, changing the parameter representation to reduce computational complexity while preserving essential behavioral patterns for recognition
Solution Approach 2:
The system extracts only the most relevant features from video data and maps them to symbolic representations, removing redundant information and reducing the computational burden while maintaining recognition reliability
2Measurement precision
If surveillance systems process large volumes of video data with detailed analysis, then the system can accurately recognize behaviors, but the system consumes significant processor power, storage, and bandwidth
Solution Approach 1:
The patent segments video data into discrete feature vectors and further segments the feature space into clustered regions, enabling efficient processing by analyzing only essential characteristics rather than complete video frames
Solution Approach 2:
The system creates symbolic representations (copies) of behavioral patterns from normalized feature vectors, allowing the system to work with compact symbolic data instead of large-volume raw video data while preserving detection accuracy
3Productivity
If the system uses cluster-based mapping of normalized vectors to generate symbols, then the system can reduce resource consumption and improve scalability, but the system requires complex processing of feature distributions
Solution Approach 1:
The patent implements dynamic cluster formation and adaptation where cluster boundaries and distributions are continuously refined based on incoming data streams, allowing the system to adapt to new behaviors while maintaining efficient symbolic representation
Data Source
AI summary
Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.