Queuing Recommendation System Using Image Processing for Queue Length Optimization
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Solution Overview
Problem
In public environments like supermarkets and hospitals, customers face prolonged queuing and waiting times due to large crowds, leading to anxiety and a reduced shopping experience, as existing solutions such as manual guidance and self-service checkout devices are inefficient and unreliable.
Innovation Solution
A queuing recommendation method and device that acquires image information of queuing objects, determines their positions, and identifies the queue with the fewest people by using image processing and deep learning algorithms to recommend the shortest wait time, incorporating modules for effective queue determination and recommendation result generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual guidance or self-service checkout devices are used, then queuing management is provided, but the efficiency is low and reliability is poor
Solution Approach 1:
The patent replaces manual guidance (mechanical human operation) with an automated image processing system using cameras and computer vision algorithms to detect, track, and analyze queuing objects, thereby improving both efficiency and reliability of queuing management
Solution Approach 2:
The system enables self-service by automatically capturing image information, determining positions, identifying queues, and providing recommendations without requiring manual intervention from staff or complex customer interaction with self-service devices
2Quantity of substance
If the number of customers is large, then more queuing capacity is needed, but the waiting time is prolonged and customer anxiety increases
Solution Approach 1:
The system implements real-time feedback by continuously monitoring queue lengths through image processing and dynamically recommending the shortest queue to customers, enabling informed decision-making that reduces waiting time without requiring reduction in customer volume
Solution Approach 2:
The system performs preliminary analysis of all queue lengths before customers make their selection, using image processing to pre-determine which queue is shortest and providing this information to customers in advance, allowing them to choose optimally before joining a queue
3Adaptability or versatility
If multiple queues are present, then more service channels are available, but customers cannot find the queue with fewest people and position information is lost
Solution Approach 1:
The system provides multi-functional capability by simultaneously detecting multiple queues, tracking individual positions within queues, determining queue directions, and providing comprehensive recommendations all through a single image processing system, making the solution adaptable to varying numbers of queues and service channels
Solution Approach 2:
The patent introduces an intermediary information processing system that captures raw image data, processes it through object detection and tracking algorithms, and transforms it into meaningful queue position information and recommendations, bridging the gap between physical queues and customer decision-making
Data Source
AI summary
The present disclosure provides a queuing recommendation method and device, a terminal and a computer readable storage medium, and belongs to the technical field of image processing. The method includes: acquiring image information of all queuing objects, acquiring position information of each queuing object according to the image information, determining all effective queues according to the position information of the queuing objects, and determining tail position information of queues where the effective queues are located, wherein each effective queue includes at least one effective queuing object, and the tail position information of the queue including the fewest effective queuing objects is used as a recommendation result. According to the queuing recommendation method provided by the present disclosure, the accuracy of the recommendation result is improved, and time is saved.