Video Surveillance Intent Prediction With Simulated Future Activity
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Solution Overview
Problem
Video surveillance systems face challenges in automatically predicting future activities of individuals of interest, requiring manual review of multiple video streams to identify potential incidents, which is inefficient and time-consuming.
Innovation Solution
Implementing a video surveillance system that utilizes AI models to analyze video streams, classify intents of individuals, identify persons of interest, and generate simulated video streams predicting their future activities using Generative AI models for display on operator consoles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of multiple video streams is used to identify potential incidents, then operators can directly observe actual events, but the process is inefficient and time-consuming
Solution Approach 1:
The patent replaces manual mechanical review of video streams with an automated AI-based system. The AI models analyze video data, identify persons of interest, predict future activities, and generate alerts automatically, eliminating the need for operators to manually review multiple video streams while maintaining effective surveillance capability
Solution Approach 2:
The surveillance system performs self-service by automatically analyzing video streams, identifying suspects, predicting their actions, and generating alerts without requiring continuous human intervention. The system monitors itself and takes proactive measures to enhance security autonomously
2Reliability
If AI models are used to predict future activity of persons of interest, then proactive response is enabled, but system complexity increases
Solution Approach 1:
The patent segments the complex surveillance system into distinct functional modules: video stream processing module, AI intent classification module, person of interest identification module, future activity prediction module, and alert generation module. This segmentation allows each component to perform its specific function independently, making the overall complex system more manageable and maintainable
Solution Approach 2:
The patent introduces AI models as intermediary components between video stream data and operational alerts. These AI models process raw video data, identify patterns, predict future activities, and translate them into actionable alerts, serving as a mediator that simplifies the interaction between complex data processing and user-friendly outputs
3Adaptability or versatility
If multiple video streams are reviewed manually, then comprehensive coverage is achieved, but operator workload increases
Solution Approach 1:
The patent replaces the mechanical manual review process with automated AI-based analysis that processes multiple video streams simultaneously. The AI system maintains comprehensive coverage by analyzing all video feeds concurrently while eliminating the operational burden on personnel through automation
Data Source
AI summary
Video analytics are used to identify people in a plurality of video streams. Trained Artificial Intelligence (AI) models are used to identify an intent of each of the identified people based on the plurality of video streams. A person of interest is identified as a person having an intent of concern. For the person of interest, recorded video streams are processed to identify past activities of the person of interest and trained AI models are used to predict a future activity of the person of interest. A description of the predicted future activity of the person of interest is provided to a Generative AI model that generates a simulated video stream of the person of interest performing the predicted future activity. The simulated video stream is displayed on an operator console.