Behavior Recognition Linguistic Modeling for Scalable Surveillance
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Solution Overview
Problem
Current surveillance systems require advance knowledge of specific behaviors to recognize them and consume significant computing resources, making them difficult to scale.
Innovation Solution
A behavior recognition system that generates a linguistic model by analyzing normalized data using neural networks to identify patterns without predefined rules, allowing it to learn and recognize behaviors over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surveillance systems use predefined rules and hard-coded definitions to recognize specific behaviors, then behavior recognition accuracy is improved, but system complexity and difficulty to adapt to new behaviors increases
Solution Approach 1:
The system performs self-learning by automatically analyzing video data to discover behavior patterns without requiring manual programming of behavior definitions. The behavioral recognition model trains itself on observed data, enabling the system to adapt to new behaviors autonomously while maintaining recognition accuracy.
Solution Approach 2:
The system transforms the approach from fixed behavioral parameters to dynamic parameter learning. Instead of hard-coding behavior definitions, the system adjusts its recognition parameters through continuous learning from video data, allowing it to adapt to varying behavior patterns without increasing system complexity.
2Measurement precision
If traditional surveillance systems process full-resolution video data to ensure comprehensive analysis, then detection precision is improved, but computing resource consumption increases
Solution Approach 1:
The system segments video processing into multiple stages: initial low-resolution screening to identify regions of interest, followed by focused high-resolution analysis only on those specific regions. This segmentation allows comprehensive detection precision while significantly reducing overall computing resource consumption by avoiding full-resolution processing of entire video frames.
Solution Approach 2:
The system applies partial processing by analyzing only the necessary portions of video data at high resolution. Instead of processing all video data comprehensively, it performs excessive analysis only on regions where behavior patterns are detected, maintaining detection precision while reducing total computational load.
3Area of stationary object
If traditional surveillance systems deploy multiple cameras and high-resolution processing to cover larger areas, then surveillance coverage is improved, but system scalability decreases due to resource requirements
Solution Approach 1:
The system enables scalable deployment by allowing each camera or processing unit to independently learn and adapt to local behavior patterns. This self-learning capability means that additional cameras can be deployed to expand coverage without requiring centralized reconfiguration or increased computational resources, improving scalability while maintaining coverage.
Solution Approach 2:
The behavioral recognition model serves multiple functions across different cameras and locations with a single unified system. The same learning algorithm adapts to various environments and behavior types, allowing the system to scale to larger surveillance areas without requiring location-specific customization, thereby improving both coverage and scalability.
Data Source
AI summary
Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.