Machine-Vision Queue Tracking for Accurate 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 processes video to estimate wait times by tracking the average crossing-time interval of persons through a service queue, filtering out non-waiting individuals, and reducing computational overhead by not continuously tracking every person.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human monitoring is used to estimate customer wait times, then the system can operate with simple infrastructure, but the accuracy and reliability of estimation deteriorates due to human error and cost
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 captures images, identifies customers waiting for service, and calculates wait times automatically, eliminating human error while maintaining relatively simple infrastructure through the use of standard computer vision technology
Solution Approach 2:
The system enables self-service monitoring where the machine vision system autonomously performs the entire wait time estimation process without human intervention. The automated detection and tracking of customers allows the system to monitor itself and provide continuous accurate data without requiring human observers
2Measurement precision
If continuous tracking of every person is performed, then the measurement precision of wait times is improved, but the computational overhead and cost increases
Solution Approach 1:
The patent extracts and focuses only on the relevant subset of people who are actually waiting for service, rather than tracking every person in the region. The machine vision system identifies and filters to track only customers in the service queue, eliminating unnecessary computational resources spent on people who are not waiting for service
Solution Approach 2:
The system segments the population into different categories (customers waiting for service vs. other people in the region) and applies tracking only to the relevant segment. This segmentation allows precise measurement of wait times for the target group while reducing overall computational overhead by excluding irrelevant individuals from continuous tracking
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.