Automated Queue Detection Using Content-Sensitive Object Detectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting waiting queues are often manual, prone to errors due to dynamic backgrounds, and require additional hardware or complex image processing steps, making them inefficient for real-time monitoring and varying queue shapes.
Innovation Solution
An apparatus with a data processing device and object detector module that connects to an image source, such as a camera, identifies objects directly in monitoring images without background subtraction, using content-sensitive detectors and modeling modules to analyze and model queue shapes, including curved and branched queues, with optional additional features like movement analysis and depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If background subtraction is used to detect queues, then queue detection can be performed, but the system becomes susceptible to errors due to dynamic backgrounds
Solution Approach 1:
The patent extracts and removes the background subtraction step from the queue detection process. Instead of subtracting a learned background image, the system directly detects objects in the monitoring image using content-sensitive detectors, thereby eliminating the source of errors related to dynamic backgrounds
Solution Approach 2:
The patent introduces queue shape models as an intermediary between object detection and queue identification. These models (straight line, curved line, branched structures) serve as templates that directly match detected objects to identify queues, bypassing the need for background subtraction entirely
2Reliability
If manual analysis is used to detect queues, then queue detection can be performed, but productivity is reduced
Solution Approach 1:
The system performs automated queue detection without human intervention. The evaluation device automatically detects objects, identifies queue shapes using models, and determines queue characteristics, enabling real-time monitoring and eliminating the need for manual analysis by checkout staff
Solution Approach 2:
The patent replaces manual visual analysis with an automated computer vision system. The evaluation device uses object detectors and queue shape models to automatically identify queues, substituting human labor with automated image processing and pattern recognition algorithms
3Measurement precision
If stereo camera systems are used to count objects, then depth information can be obtained, but device complexity and hardware costs increase
Solution Approach 1:
The system uses a standard monitoring camera (mono-image camera) that serves multiple purposes: detecting objects, determining their positions, and identifying queue shapes. This single device replaces the need for complex stereo camera systems, achieving the same functionality with simpler, more cost-effective hardware
Solution Approach 2:
The patent uses queue shape models (straight line, curved line, branched structures) as templates to match against detected objects. These model copies allow the system to identify queue patterns without requiring complex depth sensing hardware, achieving accurate queue detection through pattern matching rather than stereo vision
Data Source
AI summary
In daily life, people are often forced to join a queue in order, for example, to pay at a checkout or to be dealt with at an airport, etc. Because of the various forms of a queue, these are not usually recorded automatically, but are analyzed manually. For example, if a long queue is formed at a supermarket, as a result of which the predicted waiting time for the customers rises above a threshold value, this situation can be identified by the checkout personnel, and a further checkout can be opened. A device 1 is proposed for identification of a queue 2 of objects 10 in a monitoring area, having an interface 6 which can be connected to an image source 7, with the interface 6 being designed to observe at least one monitoring image 3 of the monitoring area of the image source, wherein the monitoring image 3 shows a scene background of the monitoring area with possible objects 10, having an evaluation device 5 which is designed to identify the queue 2 of the objects 10 in the at least one monitoring image, wherein the evaluation device 5 has an object detector module 8 which is designed to detect a plurality of objects 10 on the basis of the monitoring image 3, wherein the plurality of the detected objects 10 forms the basis for identification of the queue 2 of the objects 10, wherein the object detector module 8 is designed to identify the objects 8 in the monitoring image with the scene background and/or wherein the object detector module 8 has content-sensitive detectors 9 for detection of the objects 10.