Topview Item Tracking Using Sensor Array Homographies
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Solution Overview
Problem
Existing object tracking systems face challenges in real-time processing of multiple objects in large spaces, particularly in busy environments, due to computational intensity and the inability to accurately determine physical locations of objects within images, leading to inefficiencies in tracking and inventory management.
Innovation Solution
A tracking system that generates homographies to map pixel locations from sensors to physical locations in a global plane, enabling efficient object tracking across sensor fields of view, handoff of tracking information, detection of shelf interactions, and assignment of items to individuals using weight sensors and virtual curtains, while distinguishing between closely spaced objects and maintaining accurate inventory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If image processing techniques are used to identify objects in busy environments, then object identification capability is improved, but computational intensity increases significantly
Solution Approach 1:
The patent divides the tracking space into multiple zones covered by different sensors, with each sensor independently identifying objects in its own field of view. This segmentation allows parallel processing of multiple objects across different zones, reducing overall computational intensity while maintaining identification capability.
Solution Approach 2:
The patent introduces homography matrices as intermediary mathematical transformations that map pixel coordinates from sensor images to physical coordinates in the tracking space. This intermediary layer enables direct physical location determination without requiring complex image processing to identify every object feature, reducing computational burden.
2Area of stationary object
If multiple cameras are installed to cover larger physical spaces, then coverage area is improved, but system complexity increases
Solution Approach 1:
The patent creates a unified tracking space coordinate system that serves as a universal reference frame for all sensors. Each sensor uses the same homography transformation method to map its pixel coordinates to this universal physical space, allowing multiple cameras to work together as a single integrated system rather than separate independent systems, thus reducing overall complexity.
Solution Approach 2:
The patent transforms the problem from managing multiple independent 2D image coordinates to a unified 2D physical tracking space through homography transformations. By changing the dimensionality of the coordinate representation, the system can easily combine data from multiple sensors without complex coordination, reducing system complexity.
3Difficulty of detecting and measuring
If conventional image processing is used to track multiple objects simultaneously, then object tracking capability is improved, but processing time increases
Solution Approach 1:
The patent pre-calculates and stores homography matrices for each sensor based on their installation positions and orientations. This preliminary action allows the system to directly transform pixel coordinates to physical coordinates during real-time tracking without performing complex image processing calculations, significantly reducing processing time while maintaining tracking capability.
4Area of stationary object
If sensors are added to track positions in larger spaces, then tracking coverage is improved, but information processing complexity increases
Solution Approach 1:
The patent merges the coordinate systems of multiple sensors into a single unified tracking space through homography transformations. All sensor data is transformed to the same physical coordinate system, allowing information from multiple sensors to be combined and processed as a single integrated dataset rather than separate independent datasets, reducing information processing complexity.
Data Source
AI summary
An object tracking system includes a sensor, a weight sensor, and a tracking system. The sensor is configured to capture a frame of at least a portion of a rack within a global plane for a space. The tracking system is configured to detect a weight decrease on the weight sensor. The tracking system is further configured to receive the frame of the rack, to determine a pixel location for a person, to determine the person is within a predefined zone associated with the rack. The tracking system is further configured to identify the item associated with the weight sensor and to add the identified item to a digital cart associated with the person.