Draw Wire Encoder Homography for Real-Time Object Tracking
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Solution Overview
Problem
Existing object detection and tracking systems face challenges in real-time processing of multiple objects in large physical spaces, particularly in busy environments, due to computational intensity and the inability to determine physical locations of objects within images, leading to incompatibility with real-time applications like video streams.
Innovation Solution
A draw wire encoder based homography system that maps pixel locations from sensors to physical locations in a global plane, enabling efficient tracking of objects across sensor fields of view, handling object movement between sensors, and detecting interactions with shelves and items, using markers and virtual curtains to associate items with individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing techniques are used to identify and track objects in busy environments, then object detection capability is improved, but computational intensity increases and real-time processing becomes intractable
Solution Approach 1:
The patent introduces draw wire encoders as intermediary devices that directly measure physical positions of objects and people in the store. These encoders act as mediators between the physical space and the tracking system, providing precise position data without requiring complex image processing. The encoders convert physical movements into electrical signals that can be processed in real-time, thus maintaining detection precision while reducing computational burden.
Solution Approach 2:
The patent replaces the mechanical vision-based object detection system with an encoder-based measurement system. Instead of using cameras and image processing algorithms to identify and track objects, the system uses draw wire encoders that mechanically measure positions. This substitution eliminates the need for computationally intensive image analysis while maintaining accurate tracking capability.
2Area of stationary object
If multiple sensors are deployed to cover large physical spaces, then tracking coverage is improved, but system complexity and computational load increase
Solution Approach 1:
The patent divides the large physical space into multiple zones, each covered by a single draw wire encoder. Instead of deploying a dense array of sensors throughout the space, the system segments the area and assigns one encoder per zone. This segmentation reduces the overall system complexity while maintaining comprehensive coverage, as each encoder independently handles position measurement in its designated area without requiring coordination with numerous other sensors.
3Measurement precision
If pixel locations in sensor images are used to track objects, then object position detection is improved, but the ability to determine real physical locations is lost
Solution Approach 1:
The patent changes the measurement parameter from pixel coordinates to physical distance measurements. Draw wire encoders directly measure physical positions in real-world units (meters, feet) rather than capturing images with pixel coordinates. This parameter change preserves the precision of position detection while simultaneously providing meaningful physical location information that can be used for real-time tracking and analysis without requiring image processing transformations.
Data Source
AI summary
A sensor calibration system configured to receive a first frame of one or more markers on a repositionable platform at a first location within a space from a sensor. The system is further configured to determine pixel locations in the first frame for a first marker and a second marker from among the one or more markers. The system is further configured to receive distance information that corresponds with a distance between the platform and distance measuring devices. The system is further configured to determine (x,y) coordinates for the first marker and the second marker based on the distance information. The system is further configured to generate a homography based on the (x,y) coordinates and pixel locations of the first marker and the second marker. The homography includes coefficients that translate between pixel locations in the first frame of the sensor and (x,y) coordinates in the global plane.