Dynamic Queue Thresholding for Image Monitoring
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Solution Overview
Problem
Conventional queue analyzing methods set fixed distance thresholds, leading to errors in counting individuals in or out of a queue, as they cannot adapt to varying situations, causing misjudgment of queue length and composition.
Innovation Solution
A queue analyzing method that automatically generates an interval threshold based on object position variations within an image by computing intervals, dividing them into ranges, calculating mean and amending values, and marking adjacent objects conforming to the threshold, using an image monitoring apparatus with an image receiver and operation processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed distance threshold is set for queue analysis, then the queue analysis process is simple, but the accuracy of queue length measurement deteriorates due to inability to adapt to varying situations
Solution Approach 1:
The patent transforms the static fixed threshold into a dynamic adaptive threshold that automatically adjusts based on actual queue conditions. The system computes intervals between adjacent objects, divides them into groups, and calculates statistical parameters (mean and standard deviation) to generate thresholds that adapt to different queue densities and scenarios, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent changes the threshold parameter from a fixed constant to a dynamically computed value based on statistical analysis of object intervals. By calculating the mean and standard deviation of intervals and using them to generate adaptive thresholds, the system enables the threshold parameter to change according to actual queue conditions, improving measurement precision while maintaining automated operation.
2Quantity of substance
If a large distance threshold is used, then more people are included in the queue count, but non-queue people nearby are mistakenly counted, reducing accuracy
Solution Approach 1:
The patent implements feedback through statistical analysis of computed intervals. The system calculates intervals between all adjacent objects, uses these intervals to compute mean and standard deviation, and feeds this statistical information back to generate an adaptive threshold. This feedback mechanism enables the system to learn from actual data and adjust the threshold to exclude non-queue people while including legitimate queue members.
Solution Approach 2:
The patent performs preliminary statistical analysis of object intervals before finalizing the threshold. By pre-computing the distribution of intervals, dividing them into groups, and calculating statistical parameters in advance, the system prepares the optimal threshold that balances inclusivity and accuracy before actual queue analysis, preventing both over-counting and under-counting.
3Measurement precision
If a small distance threshold is used, then queue analysis accuracy improves by excluding non-queue people, but legitimate queue members are excluded, causing queue length misjudgment
Solution Approach 1:
The patent makes the threshold dynamic rather than fixed, allowing it to adjust based on the statistical distribution of intervals. The system computes intervals, analyzes their distribution through grouping and statistical calculation, and generates a threshold that dynamically adapts to the specific queue scenario. This prevents both the inclusion of non-queue people and the exclusion of legitimate queue members.
Solution Approach 2:
The patent enables the system to automatically determine the appropriate threshold through self-service statistical analysis. By computing intervals from actual object positions, analyzing the interval distribution, and generating the threshold autonomously without manual intervention, the system serves itself to find the optimal balance between accuracy and completeness for each specific scenario.
Data Source
AI summary
A queue analyzing method is applied to an image monitoring apparatus and can automatically generate an interval threshold according to position variation of objects. The queue analyzing method includes computing a plurality of intervals between all objects inside an image, dividing the plurality of intervals at least into a first group corresponding to a low interval range and a second group corresponding to a high interval range, computing an interval mean value and an interval amending value of objects inside the first group, utilizing the interval mean value and the interval amending value to generate the interval threshold, and marking some adjacent objects conforming to the interval threshold within the image.