Video Surveillance AI for Predicting Person-of-Interest Activity
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Solution Overview
Problem
Existing video surveillance systems face challenges in automatically analyzing video streams to predict the future activities of individuals of interest, making it difficult for operators to anticipate and respond to potential incidents.
Innovation Solution
Implementing a video surveillance system that utilizes AI models to identify and classify the intent of individuals, predict their future activities, and generate simulated video streams of those activities using Generative AI, displayed on an operator console for proactive monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of video streams is used, then operator control and judgment are maintained, but the system cannot keep up with the large volume of video data and cannot predict future activity
Solution Approach 1:
The system enables automatic self-analysis of video streams through AI models that independently detect activities, identify persons of interest, and predict future actions without requiring continuous manual intervention, thereby increasing productivity while maintaining operational autonomy
Solution Approach 2:
The patent replaces manual visual inspection with AI-based computer vision and machine learning models that automatically process video data, enabling the system to handle large volumes of video streams and predict future activity that would be impossible through manual review alone
2Reliability
If AI models are used to predict future activity, then proactive monitoring capability is improved, but system complexity increases
Solution Approach 1:
The system divides the complex prediction task into separate functional modules: video stream processing, activity detection, person identification, intent classification, and future activity prediction. Each module handles a specific aspect of analysis, making the overall complex system more manageable and maintainable while improving prediction reliability
Solution Approach 2:
The patent introduces intermediate processing layers including feature extraction modules and intent classification models that bridge the gap between raw video data and final predictions. These intermediaries simplify the relationship between input data and output predictions, making the system more reliable while managing complexity through structured architecture
3Area of stationary object
If multiple video cameras are deployed throughout the facility, then coverage and monitoring capability are improved, but the difficulty of manually reviewing all video streams increases
Solution Approach 1:
The system automatically processes video streams from multiple cameras without requiring operator intervention, using AI models to independently analyze each stream, identify persons of interest, and generate predictions. This self-service capability maintains comprehensive facility coverage while eliminating the operational burden of manually reviewing numerous video streams
Solution Approach 2:
The patent extracts and isolates specific information from the video streams, such as person locations, activities, and predicted actions, separating this critical data from the bulk video data. This extraction allows the system to maintain comprehensive monitoring of the entire facility while presenting only the essential information that requires operator attention
Data Source
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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.