Video Queue Analysis System for Retail Staffing Optimization
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Solution Overview
Problem
Current video security systems face inaccuracies in estimating queue lengths and wait times due to occlusions, grouping issues, and the complexity of integrating radar data, especially when calibrating camera angles and measuring vehicle speeds, which affects the reliability of queue analysis in various environments.
Innovation Solution
The system defines queue regions within a video analysis system by determining spatial overlap and allows operators to draw queue regions over video data, enabling the differentiation of short, medium, and long queue lengths, and sets event triggers for critical events, thereby improving the accuracy of queue monitoring and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar-based sensors are integrated with video data to determine vehicles in a queue, then measurement precision is improved, but device complexity increases due to calibration requirements between radar sensors and video cameras
Solution Approach 1:
The patent extracts and removes the radar sensor integration requirement from the queue analysis system. By relying solely on video data and artificial intelligence algorithms, the system eliminates the need for complex multi-sensor calibration while maintaining accurate queue length measurement through computer vision techniques.
Solution Approach 2:
The patent replaces the mechanical/radar-based detection system with an optical/computer vision-based system. Instead of using radar sensors to detect vehicles, the system uses video cameras combined with AI algorithms to identify and track vehicles in queues, simplifying the overall system architecture.
2Manufacturing precision
If video camera angle and positioning are carefully selected to divide scenes into human-sized slots, then manufacturing precision is improved, but ease of operation deteriorates as distance between individuals and camera increases
Solution Approach 1:
The patent implements dynamic queue region adjustment based on detected queue length. The system automatically adapts the size and positioning of queue regions in response to changing queue conditions, eliminating the need for fixed camera positioning and manual calibration for different distances.
Solution Approach 2:
The patent changes the parameters of queue regions dynamically based on detected objects and queue characteristics. Instead of using fixed human-sized slots that require precise camera positioning, the system adjusts region parameters automatically to accommodate varying distances and queue configurations.
3Productivity
If the system divides scene into slots to detect queue based on motion across slots, then productivity is improved, but measurement precision deteriorates due to occlusion and grouping issues
Solution Approach 1:
The patent segments the queue detection process into multiple independent queue regions rather than using a single divided scene approach. Each queue region is analyzed separately, allowing the system to maintain high detection speed while improving individual identification accuracy by focusing computational resources on specific regions.
Solution Approach 2:
The patent transitions from a two-dimensional slot division approach to a multi-region spatial segmentation approach. By defining multiple queue regions with different characteristics and analyzing them in parallel, the system achieves both high productivity and improved measurement precision.
4Ease of operation
If aggregate of transaction processing times is used to estimate individual wait time, then ease of operation is improved, but measurement precision deteriorates due to difficulty identifying items presented by individuals
Solution Approach 1:
The patent introduces queue regions as intermediary zones between individuals and the service point. By tracking which queue region an individual occupies and how long they remain in each region, the system estimates wait times based on spatial-temporal patterns rather than requiring detailed item identification, maintaining simplicity while improving precision.
Data Source
AI summary
A system and method for analysing queues in frames of video enables operators to preferably draw three regions of interest overlaid upon the video as short, medium, and long queue regions that form a notional queue area within the video. The regions are drawn with knowledge of, or in anticipation of, foreground objects such as individuals and vehicles waiting for service in a queue. Examples include retail point of sale locations or for automated teller machine (ATM) transactions. In conjunction with a video analytics system that analyses the movement of the foreground objects relative to the queue regions, the system determines the number of objects occupying each queue region, length of the queue, and other queue-related statistics. The system can then create reports and send messages that include the queue analysis results for directing operators to change their staffing resources as part of a real-time queue servicing and optimization response.