Video Tracking for Drive-Thru Queue Sequence Accuracy
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Solution Overview
Problem
The random merging of customers into a single queue lane in tandem ordering systems can lead to mistakes in the order-fulfillment process, such as incorrect charging or item delivery, due to the inefficiency of manual re-sequence adjustments, which wastes employee time and can result in lost revenue.
Innovation Solution
An automated method and system that uses video data from multiple image sources to track subjects in a queue area, identifying their movement and sequence relative to a predefined merge point, allowing for the accurate updating of the event sequence to match the observed order in the single queue lane, thereby ensuring correct fulfillment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual re-sequencing is used to match customer sequence to order sequence, then order fulfillment accuracy can be maintained, but employee time and operational efficiency are reduced
Solution Approach 1:
The patent replaces the manual mechanical process of employees re-sequencing orders with an automated optical system using cameras and image processing algorithms. The system automatically captures images of customers at the merge point, identifies them, and re-sequences the order queue to match the actual customer sequence, eliminating manual intervention while maintaining accuracy.
Solution Approach 2:
The system enables the order sequencing process to be self-performing through automated customer identification and tracking. The computer system automatically detects customer positions, determines the correct sequence, and updates the order queue without requiring employee intervention, allowing the system to service itself.
2Reliability
If manual re-sequencing operations are performed, then order sequence can be corrected, but operational complexity and potential for errors increase
Solution Approach 1:
The patent replaces complex manual re-sequencing operations with an automated computer vision system. The system uses cameras to capture customer images, processes these images to identify customers and determine their sequence, and automatically updates the order queue. This substitution eliminates the complexity of manual tracking and re-sequencing operations.
Solution Approach 2:
The patent introduces an automated image processing system as an intermediary between customer arrival and order fulfillment. This intermediary automatically captures customer positions at the merge point, processes the information to determine correct sequencing, and communicates the updated sequence to the fulfillment system, simplifying the overall process.
3Reliability
If employees are required to manage re-sequencing tasks, then order accuracy can be maintained, but productivity and throughput are reduced
Solution Approach 1:
The system performs the re-sequencing function autonomously without requiring employee time. The automated system continuously monitors customer positions, automatically updates order sequences in real-time as customers merge into the single queue lane, and maintains accurate order fulfillment, freeing employees to focus solely on customer service and increasing overall throughput.
Solution Approach 2:
The automated system operates continuously without interruption, constantly monitoring customer positions and updating sequences in real-time. This continuous operation ensures order accuracy is maintained at all times while eliminating the periodic interruptions that would occur with manual re-sequencing, thereby maximizing productivity and throughput.
Data Source
AI summary
A method for updating an event sequence includes acquiring video data of a queue area from at least one image source; searching the frames for subjects located at least near a region of interest (ROI) of defined start points in the video data; tracking a movement of each detected subject through the queue area over a subsequent series of frames; using the tracking, determining if a location of the a tracked subject reaches a predefined merge point where multiple queues in the queue area converge into a single queue lane; in response to the tracked subject reaching the predefined merge point, computing an observed sequence of where the tracked subject places among other subjects approaching an end-event point; and, updating a sequence of end-events to match the observed sequence of subjects in the single queue lane.