Distributed Video Analytics for Dwell Time and Facial Recognition
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Solution Overview
Problem
Current video monitoring systems face challenges in efficiently determining dwell time and facial recognition for individuals in monitored areas, particularly in large and complex security camera systems, which can strain network bandwidth and require extensive data processing.
Innovation Solution
A distributed video analytics system that includes a master controller and workers with facial recognition neural networks, capable of receiving images from multiple cameras, detecting individuals, and calculating dwell time, while managing watchlists and alerting for threshold exceedances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a distributed video analytics system with multiple workers is used, then processing capacity and scalability are improved, but system complexity increases
Solution Approach 1:
The system divides the video analytics processing into multiple independent worker nodes, each capable of handling facial recognition and dwell time calculations for specific camera feeds. This segmentation allows the system to scale processing capacity by adding more workers without requiring a complete system redesign, while each worker maintains manageable complexity through standardized interfaces with the master controller.
2Measurement precision
If facial recognition and dwell time monitoring are performed for all detected individuals, then security monitoring accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The system applies different processing levels to different individuals based on their characteristics and behavior patterns. Facial recognition is performed with varying degrees of intensity, and dwell time monitoring is activated selectively for individuals who meet specific criteria such as lingering in restricted areas or matching watchlist profiles, rather than uniformly processing all detected persons.
Solution Approach 2:
The system performs comprehensive facial recognition and monitoring only when necessary - such as when an individual is detected in sensitive areas, matches a watchlist profile, or exhibits suspicious behavior patterns. For routine situations, the system uses lighter processing modes, reducing network bandwidth consumption while maintaining security effectiveness through targeted detailed analysis.
Data Source
AI summary
Systems and methods for determining dwell time is provided. The method includes receiving images of an area including one or more people from one or more cameras, and detecting a presence of each of the one or more people in the received images using a worker. The method further includes receiving by the worker digital facial features stored in a watch list from a master controller, and performing facial recognition and monitoring the dwell time of each of the one or more people. The method further includes determining if each of the one or more people is in the watch list or has exceeded a dwell time threshold.