Video Analysis System for Social Distancing Enforcement
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Solution Overview
Problem
Current video monitoring systems lack an efficient and automated method to track social distancing violations and identify persons-of-interest in video feeds, requiring manual labor and being ineffective in monitoring large areas, especially during pandemics like COVID-19.
Innovation Solution
A video analysis system that uses cameras to monitor video frames, calculates distances between individuals, compares these distances to a threshold, and generates alerts for social distancing violations, while also tracing contacts of persons-of-interest using facial recognition and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual monitoring of video feeds is used to identify persons-of-interest and track social distancing violations, then detection accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical review of video recordings with automated computer vision algorithms and machine learning models that can process video feeds in real-time, automatically detecting persons-of-interest and measuring social distancing compliance without human intervention
Solution Approach 2:
The system enables self-service monitoring where the video analysis system autonomously performs detection, tracking, and measurement functions without requiring human operators to manually review footage, with the system automatically generating alerts and reports
2Extent of automation
If manual tracking of contacts is performed to identify persons within two degrees of separation, then contact tracing can be achieved, but labor requirements and time consumption become prohibitive
Solution Approach 1:
The patent replaces manual contact tracking with automated computer vision algorithms that can identify and track persons-of-interest and their contacts across multiple video feeds simultaneously, automatically measuring distances and determining compliance without human labor
Solution Approach 2:
The system extends monitoring beyond simple spatial proximity by incorporating temporal dimensions through video frame analysis and sequential tracking, enabling automatic identification of contact patterns across time and multiple camera views
3Area of stationary object
If multiple cameras are deployed to monitor large areas for social distancing compliance, then coverage area increases, but system complexity and cost increase
Solution Approach 1:
The patent implements a unified video analysis system that can process multiple video feeds from different cameras simultaneously using the same computer vision algorithms and machine learning models, enabling the system to handle large areas without proportionally increasing complexity
Solution Approach 2:
The system merges multiple video feeds into a coordinated monitoring network where a centralized or distributed processing system analyzes all feeds together, sharing computational resources and algorithms across the entire monitored area rather than treating each camera independently
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer readable mediums directed to identifying persons monitored on video frames of a video feed, determining if the number of people on a monitored video frame is above a prescribed group threshold, and if so, calculating distances between each respective person in the monitored video frame. The calculated distances between each respective person are then compared to a prescribed distance threshold and alerts are generated in response to any calculated distance not satisfying the prescribed threshold. Identification of persons in a monitored video frame may be performed by, for example, facial recognition software. Calculation of distance between persons in a monitored video frame may be performed, for example, by using bounding boxes, machine learning, or other classification techniques.