Virtual Security Agent for Real-Time Video Anomaly Detection
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Solution Overview
Problem
Existing building security systems lack the ability to accurately and efficiently generate relevant data for specific security threats and conditions due to limitations in generative AI models, such as imprecision, lack of transparency, and resource-intensive data processing, which hinders effective anomaly detection and response.
Innovation Solution
Implementing a system that utilizes generative AI models, like LLMs and multi-modal models, to process unstructured data from various sources, integrating expert feedback and automated thresholds for real-time anomaly detection and response, enabling accurate and efficient security operations through conversational interfaces and automated report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI models are used to process unstructured security data, then data processing capability is improved, but computational resource requirements increase
Solution Approach 1:
The system segments data processing by using multiple specialized machine learning models (e.g., computer vision models for video analysis, NLP models for text processing, audio models for sound detection) rather than one large generative AI model. Each model processes specific types of unstructured data, improving overall processing capability while distributing computational load across multiple smaller, more efficient models.
Solution Approach 2:
The system introduces an intermediary layer that converts unstructured data (videos, images, audio, text) into structured formats before analysis. This intermediary processing step enables more efficient computation by transforming complex unstructured data into organized representations that can be processed with lower computational resources while maintaining high processing capability.
2Speed
If machine learning models perform real-time anomaly detection, then detection speed is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-training multiple specialized machine learning models on extensive datasets before deployment. These models are pre-configured with knowledge of normal and abnormal patterns, enabling them to perform real-time detection with high accuracy without requiring complex runtime computations that would slow down processing.
Solution Approach 2:
The system implements feedback mechanisms where detected anomalies are reviewed and validated through multiple layers of analysis. The models continuously learn from feedback loops, adjusting their detection thresholds and parameters to maintain high precision while operating in real-time. This feedback-driven approach ensures that detection speed does not compromise accuracy.
3Adaptability or versatility
If multiple machine learning models are deployed for comprehensive security monitoring, then detection coverage is improved, but device complexity increases
Solution Approach 1:
The system employs universal machine learning models that can handle multiple types of unstructured data (video, audio, text, images) and perform various security functions (anomaly detection, threat classification, incident reporting). This multi-functionality approach achieves comprehensive detection coverage while avoiding the complexity of deploying separate specialized models for each data type and function.
Solution Approach 2:
The system merges multiple detection functions and data processing capabilities into an integrated security monitoring platform. By combining video analysis, audio detection, text processing, and anomaly detection into a unified system with centralized control and coordination, the platform achieves comprehensive coverage while managing complexity through integrated architecture rather than separate distributed systems.
Data Source
AI summary
A building security system includes processors configured to: provide one or more machine learning models, at least one of the machine learning models trained to identify abnormalities within video data, the at least one machine learning model trained using at least one of video data or image data and annotations to the at least one of the video data or image data, and provide a virtual agent configured to: receive and process one or more input videos using the at least one machine learning model to identify abnormalities based on contextual information identified from the one or more input videos, and automatically perform, by the one or more machine learning models, an operator function in response to the identified abnormalities, wherein the operator function is determined according to at least one of a set of rules defined by the building security system or an operator input.