Machine-Vision Person Tracking for Queue Wait-Time Estimation
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Solution Overview
Problem
Existing methods for estimating customer wait times in service queues are costly and prone to errors, particularly when relying on human monitoring.
Innovation Solution
An automated system using machine vision to analyze video of service environments, estimating wait times based on the average crossing-time interval and number of persons awaiting service, while filtering out non-waiting individuals, reduces computational overhead and error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human monitoring is used to estimate customer wait times, then the estimation can be performed, but the method is costly and prone to errors
Solution Approach 1:
The patent replaces human monitoring with an automated machine vision system that uses cameras and image processing algorithms to detect and track customers in the queue. The system automatically estimates wait times by analyzing customer positions and movements, eliminating the need for manual observation and reducing human error while lowering operational costs.
Solution Approach 2:
The system enables self-service monitoring where the machine vision system autonomously performs wait time estimation without human intervention. The automated detection and tracking of customers allows the system to serve itself in monitoring queue conditions, freeing employees from manual monitoring tasks.
2Measurement precision
If continuous tracking of all individuals is performed, then accurate wait time estimation is possible, but computational overhead increases
Solution Approach 1:
The patent extracts only the necessary information for wait time estimation from the video feed, such as customer positions relative to the queue and boundary crossings. By focusing computation only on detecting and tracking customers rather than analyzing every pixel or individual movement detail, the system reduces computational overhead while maintaining measurement accuracy.
Solution Approach 2:
The system segments the queue into discrete sections or boundaries and tracks customers' positions relative to these segments. This segmentation allows the system to estimate wait times by counting how many customers are in each segment and their progression through the queue, reducing the computational complexity compared to continuous tracking of all individual movements.
3Productivity
If machine vision is used to estimate wait times, then accuracy and efficiency are improved, but the system complexity increases
Solution Approach 1:
The machine vision system serves multiple functions: it detects customers, tracks their positions, determines queue length, and estimates wait times all through a single integrated system. By making the system multi-functional, the patent reduces overall system complexity compared to having separate systems for each function, while maintaining high productivity in wait time estimation.
Data Source
AI summary
A method to predict a traversal-time interval for traversal of a service queue comprises receiving video of a region including the service queue, recognizing in the video, via machine vision, a plurality of persons awaiting service within the region, estimating an average crossing-time interval between successive crossings, by the plurality of persons, of a fixed boundary along the service queue, wherein such estimating is based on features of the service queue and of the one or more persons awaiting service, and returning an estimate of the traversal-time interval based on a count of the persons awaiting service and on the average crossing-time interval as estimated.