Sensor Array Homography for Real-Time Item Tracking
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and tracking multiple objects in real-time, especially in large physical spaces, due to computational intensity and the need for significant processing time. Additionally, these systems struggle to determine the physical location of objects within an image and handle object tracking across the field of view of multiple sensors.
Innovation Solution
The system generates a homography that maps pixels from a sensor array to physical locations in a global plane, enabling accurate object tracking and location determination. It also includes processes for handing off tracking information between sensors, detecting shelf interactions, and identifying misplaced items based on weight analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If image processing techniques are used to identify objects, then object identification capability is improved, but computational intensity and processing time increase significantly
Solution Approach 1:
The system segments the identification process by using multiple sensors (cameras, RFID, weight sensors) to capture different types of data simultaneously. Each sensor type processes specific aspects of object identification, dividing the computationally intensive task into parallel, less demanding sub-tasks that can be processed more efficiently
Solution Approach 2:
The patent introduces homography matrices as intermediary mathematical transformations that map sensor data coordinates to physical space coordinates. This intermediary step enables the system to determine physical locations without requiring complex full-image processing, reducing computational intensity while maintaining identification accuracy
2Area of stationary object
If multiple sensors are deployed to cover large physical spaces, then tracking coverage is improved, but system complexity increases
Solution Approach 1:
The system employs multi-functional sensors that can perform multiple tasks simultaneously. For example, cameras not only capture images for object identification but also provide spatial information through homography transformations. Weight sensors on shelves serve both to detect item removal and to identify item locations. This multi-functionality reduces the need for additional specialized sensors, thereby managing system complexity while expanding coverage
Solution Approach 2:
The patent merges data from multiple sensor types (visual data from cameras, RFID data, weight data) into a unified tracking system. By combining these different data streams and processing them through a common homography-based coordinate system, the system achieves comprehensive coverage without proportionally increasing complexity, as the same processing framework handles all sensor inputs
3Measurement precision
If conventional checkout processes are used, then item tracking accuracy is improved, but customer convenience and processing time worsen
Solution Approach 1:
The system implements self-service tracking where sensors automatically detect and track items as customers pick them up from shelves and place them in carts. Weight sensors on shelves detect item removal, cameras track the items through the store, and the system automatically associates items with customers without requiring manual scanning or checkout processes. This maintains tracking accuracy while eliminating the time-consuming conventional checkout procedure
Solution Approach 2:
The system performs preliminary tracking and identification of items and customers before the traditional checkout point is reached. By continuously monitoring item locations and customer movements throughout the store using the sensor array and homography-based positioning, the system has already identified what items each customer has taken by the time they reach the exit, eliminating the need for a separate checkout process
Data Source
AI summary
An object tracking system that includes a 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 further configured to receive the frame, to determine a pixel location for the first person, and to determine a person is within the predefined zone associated with the rack. The tracking system is further configured to identify the plurality of items in a digital cart associated with the person, to identify an item from the digital cart associated with the first person, and to remove the identified item from the digital cart associated with the person.