Dynamic Person Queue Analytics Using Node-Based Spatial Detection
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Solution Overview
Problem
Current person queue analytics approaches are limited by the need for pre-defined queue zones and camera calibration, making them inapplicable to dynamic and complex queuing patterns, which are common in real-world scenarios.
Innovation Solution
A dynamic approach that identifies focal points of interest and represents person queues as an ordered list of nodes based on distance and angle from a focal point, using multi-modal tracking and machine learning to accurately detect and analyze queues without pre-defined configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined queue zones and camera calibration are used, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system automatically identifies focal points of interest and detects queues based on natural human behavior patterns (orientation, proximity, direction of movement) without requiring manual configuration of queue zones or camera calibration. The algorithm self-adapts to the physical space by analyzing image data and determining spatial relationships between people and focal points automatically.
Solution Approach 2:
The system changes from using fixed, pre-defined queue zones to using dynamic parameters such as distance from focal point, orientation angles, and movement directions to identify and track queues. This allows the system to adapt to different queuing patterns without requiring reconfiguration of the physical zones.
2Measurement precision
If pre-defined queue zones and camera calibration are used, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The system performs automatic queue detection without requiring operators to define queue zones or calibrate cameras. The algorithm automatically identifies focal points (such as service counters or attractions) and detects people forming queues based on their natural positioning and orientation relative to these points, making the system as easy to operate as pointing a camera at the queue area.
3Measurement precision
If static queue zones are used, then measurement precision is improved, but adaptability worsens
Solution Approach 1:
The system transitions from static, pre-defined queue zones to dynamic queue detection that adapts to changing queuing patterns. The algorithm continuously analyzes image data to identify focal points and detect queues based on real-time spatial relationships, allowing it to handle various queue configurations including single-line, multi-line, serpentine, and free-form queues.
Solution Approach 2:
The system becomes universally applicable to different types of queues and physical spaces by using general principles of human queuing behavior (people position themselves near focal points, orient toward them, and maintain proximity) rather than requiring space-specific configurations. This allows the same system to detect queues in airports, retail stores, theme parks, and other environments without customization.
Data Source
AI summary
In one embodiment, a device identifies, from image data captured by one or more cameras of a physical location, a focal point of interest and people located within the physical location. The device forms a set of nodes whereby a given node represents one or more of the identified people located within the physical location. The device represents a person queue as an ordered list of nodes from the set of nodes and adds a particular one of the set of nodes to the list based on the particular node being within a predefined distance to the focal point of interest. The device adds one or more nodes to the list based on the added node being within an angle and distance range trailing a forward direction associated with at least one node in the list. The device provides an indication of the person queue to an interface.