Video-Based Queue Configuration Detection Using Computer Vision
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Solution Overview
Problem
Existing methods fail to automatically determine dynamic queue configurations, including shape, width, split/merge points, and wait times, which are crucial for managing customer traffic and improving business performance in environments with varying queue conditions.
Innovation Solution
A video-based method and system that acquires frames from a queue area, detects subjects, tracks their locations, and estimates queue configuration descriptors using traffic load metrics to classify and adjust queue configurations dynamically, employing variable or constant pipeline latency approaches to localize and compute queue parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple separate queues are used to reduce customer wait times, then service efficiency is improved, but perceived customer inequality increases when queue speeds differ
Solution Approach 1:
The system merges multiple separate queues into a single unified virtual queue, where customers are assigned to different physical queues but all queues feed into a common service sequence. This combination maintains high service efficiency through parallel processing while ensuring fairness by eliminating the advantage of choosing faster queues.
Solution Approach 2:
The system dynamically adjusts queue assignments and service sequencing based on real-time queue speed measurements and customer characteristics. By continuously monitoring and adapting the queue configuration, the system optimizes both service efficiency and perceived fairness under varying conditions.
2Ease of operation
If queue configuration is manually adjusted by employees to prevent overflow, then queue management control is improved, but operational complexity and labor requirements increase
Solution Approach 1:
The system implements self-service queue management through automated detection of queue configuration parameters (length, width, shape, split/merge points) using computer vision and image processing. The system autonomously monitors and adjusts queue configurations without employee intervention, eliminating manual labor while maintaining effective control.
Solution Approach 2:
The patent replaces manual mechanical queue management with an automated optical detection system using cameras and image processing algorithms. This substitution eliminates the need for employee physical intervention while providing continuous, precise monitoring of queue parameters.
3Productivity
If queue capacity is expanded by increasing length or size, then customer throughput is improved, but space requirements and infrastructure costs increase
Solution Approach 1:
The system dynamically adjusts queue capacity and configuration based on real-time traffic load detection. During peak hours, the system optimally utilizes available space by adjusting queue length, width, and segmentation. During off-peak hours, queue capacity is reduced, freeing up space while maintaining adequate throughput capability.
Solution Approach 2:
The system changes queue configuration parameters (length, width, shape, number of split/merge points) dynamically based on detected traffic conditions. This allows the queue to adapt its physical footprint to match demand, maximizing throughput during high traffic while minimizing space occupation during low traffic periods.
4Measurement precision
If automated queue parameter detection is implemented, then decision-making accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex manual measurement and estimation methods with automated computer vision and image processing systems. The automated detection of queue parameters (length, width, shape, split/merge points) provides high precision measurements while the modular software architecture manages computational complexity through efficient algorithms.
Data Source
AI summary
A method for automatically determining a dynamic queue configuration includes acquiring a series of frames from an image source surveying a queue area. The method includes detecting at least one subject in a frame. The method includes tracking locations of each detected subject across the series of frames. The method includes generating calibrated tracking data by mapping the tracking locations to a predefined coordinate system. The method includes localizing a queue configuration descriptor based on the tracking data.