Facial Recognition Queue Time Tracking via Dynamic Video Analysis
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Solution Overview
Problem
Conventional facial recognition systems are not practical for real-time scenarios, such as queue time determination, as they require individuals to stand still and face the camera, making them unsuitable for dynamic environments like travel facilities.
Innovation Solution
A method and apparatus using facial recognition cameras to track passengers by capturing and matching facial geometry from real-time video streams across different locations, with the system extracting facial templates from displays showing flight information, enabling near real-time sharing of data to determine dwell times without storing actual images, thus avoiding personally identifiable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional facial recognition systems require individuals to stand still and face the camera, then identification accuracy is improved, but real-time tracking capability deteriorates
Solution Approach 1:
The system transitions from static facial recognition (requiring stillness) to dynamic tracking by capturing facial features across multiple video frames as passengers move through the airport. The system processes sequential images to maintain identification accuracy while accommodating natural movement, enabling real-time queue time measurement without requiring passengers to stand still.
Solution Approach 2:
The system performs preliminary facial feature extraction and template creation from video streams before actual identification is needed. By pre-processing and storing facial templates in advance, the system reduces computation time during real-time tracking, allowing rapid comparison and identification as passengers move through security checkpoints.
2Reliability
If facial images are captured and stored for identification, then recognition reliability is improved, but data privacy protection deteriorates
Solution Approach 1:
The system extracts only essential facial features (geometric relationships between facial landmarks) from complete facial images. By separating and retaining only the necessary biometric data while discarding unnecessary image information, the system maintains identification reliability while minimizing privacy risks associated with storing full facial images.
Solution Approach 2:
Instead of storing actual facial images, the system creates and stores mathematical templates (compressed representations) of facial features. These templates serve as functional copies that enable identification without containing personally identifiable visual information, thus protecting passenger privacy while maintaining system reliability.
3Measurement precision
If multiple facial recognition cameras are deployed across different locations, then queue time measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The system designs cameras and processing units that serve multiple functions: capturing facial images for identification, tracking passenger movement for queue time measurement, and providing surveillance capabilities. This multi-functionality reduces the need for separate specialized equipment, thereby measuring queue time accurately without proportionally increasing system complexity.
Solution Approach 2:
The system combines facial recognition and queue time tracking functions into a unified processing framework. By merging the identification algorithm with the timing and location tracking logic, the system achieves precise queue time measurement while avoiding the complexity of separate independent systems working in parallel.
Data Source
AI summary
A method and apparatus for tracking passengers at a travel facility and may include receiving captured facial recognition features of a passenger using a first facial recognition camera at a first known location, determining if the captured facial recognition features of the passenger are stored in a database, wherein if the captured facial recognition features of the passenger are not stored in the database, starting a timer, receiving captured facial recognition features of the passenger using a second facial recognition camera at a second known location, stopping the timer, determining the amount of elapsed time between the received captured facial recognition features of the passenger using the first facial recognition camera at the first known location and the receiving captured facial recognition features of the passenger using the second facial recognition camera at the second known location, outputting the determined amount of elapsed time to at least one or more.