Intelligent Security System Using Structured AI Video Queries
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Solution Overview
Problem
Generative AI-based image recognition models face challenges in specifying the scope of results and result analysis ambiguity, leading to inaccurate security event identification.
Innovation Solution
An intelligent security system utilizing an AI video analyzer to generate specific queries, incorporating object description text and graphic information, and a security event processor to parse generative AI responses, along with a multimodal AI analyzer to integrate sensor data, enhancing query specificity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI-based image recognition models are used to analyze video and identify security events, then the system can process video data and generate security event information, but the model produces ambiguous results and cannot specify the scope of results accurately
Solution Approach 1:
The system segments the video analysis process into multiple stages: first using a discriminative model to identify objects and generate structured description information, then using this structured information as input to the generative AI model. This segmentation allows each model to perform its strength - the discriminative model provides precise object identification while the generative model provides contextual understanding - thereby resolving the contradiction between productivity and result specification accuracy.
Solution Approach 2:
The patent introduces an intermediary component - the object description information generation module - that acts as a mediator between the video input and the generative AI model. This intermediary processes video frames to extract object descriptions, locations, and contextual information, then formats this as structured query inputs for the generative AI. This intermediary ensures that the generative AI receives precise, structured information rather than raw video data, thereby improving result specification accuracy while maintaining video processing capability.
2Measurement precision
If discriminative models are used to classify video data according to certain criteria, then the scope of results can be specified, but the system lacks the ability to understand inherent meaning and future possibilities of the video content
Solution Approach 1:
The system merges the strengths of both discriminative and generative models by combining them in a hybrid architecture. The discriminative model first processes video data to provide structured object classification and scope specification, then this structured information is fed to the generative AI model which adds contextual understanding, inherent meaning analysis, and predictive capabilities. This merging allows the system to achieve both precise result scope specification and deep contextual understanding simultaneously.
Solution Approach 2:
The system performs preliminary action by using the discriminative model to pre-process and structure the video data before it is input to the generative AI model. This preliminary processing step extracts object descriptions, locations, and temporal information, creating a structured representation that the generative AI can then interpret deeply. This preliminary action ensures that the generative model receives optimized input data, thereby improving both the precision of result scope specification and the quality of inherent meaning understanding.
3Ease of operation
If the system queries generative AI with basic video descriptions, then the query process is simple, but the analysis results contain ambiguity that reduces security event identification accuracy
Solution Approach 1:
The system performs preliminary action by pre-processing the video data to extract structured object description information before forming the query to the generative AI. This preliminary extraction of object descriptions, locations, and contextual information creates a well-structured query input that simplifies the actual query process while ensuring high reliability in security event identification. The structured pre-processed data ensures that even simple queries yield accurate and unambiguous results.
Solution Approach 2:
The system changes the parameters of the query input by transforming basic video descriptions into structured, multi-parameter queries that include object descriptions, locations, temporal information, and contextual details. This parameter transformation maintains ease of operation by automating the query formation process while significantly improving reliability through the comprehensive and structured nature of the enhanced query parameters.
Data Source
AI summary
Provided is an intelligent security system, which generates a more specific query about a situation in which video is captured and query to generative AI to be able to reduce ambiguity in result analysis different from intent, thereby more accurately identifying a security event.