Homography Error Correction in 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 busy environments, such as stores, due to computational intensity and the inability to determine physical locations of objects within images, especially when objects are closely spaced or have non-uniform weights.
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 camera fields of view, detecting shelf interactions, and associating items with individuals using weight sensors and virtual curtains, while correcting homography errors and distinguishing between closely spaced objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If homography mapping is used to translate pixel locations to physical locations, then object tracking accuracy across camera fields of view is improved, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary calibration by placing markers at known physical locations before operation. During calibration, the system captures images of markers and pre-computes homography matrices that map pixel coordinates to physical coordinates. These pre-computed homographies are stored and reused during actual tracking operations, eliminating the need for real-time homography computation and significantly reducing processing time while maintaining accuracy.
2Area of stationary object
If multiple sensors are deployed to cover large spaces, then tracking coverage area is improved, but system complexity and calibration difficulty increase
Solution Approach 1:
The system uses a universal marker-based calibration approach that works consistently across all sensors regardless of their location or orientation. The same marker detection and homography computation algorithm is applied to every sensor in the array, making the calibration process scalable and systematic. This universal approach simplifies multi-sensor calibration compared to custom calibration procedures for each sensor.
Solution Approach 2:
The system divides the large tracking space into multiple zones, each covered by individual sensors. Each sensor independently calibrates its own field of view using markers placed within its coverage area. This segmentation allows each sensor to be calibrated separately, reducing the overall calibration complexity compared to attempting to calibrate the entire multi-sensor system as a single unit.
3Measurement precision
If homography correction is performed continuously to maintain accuracy, then tracking precision is improved, but computational energy consumption increases
Solution Approach 1:
Instead of continuous homography correction, the system performs calibration periodically using markers placed in the environment. The pre-computed homographies are applied repeatedly during tracking operations without requiring continuous correction computations. This periodic calibration approach maintains tracking precision while significantly reducing computational energy consumption compared to continuous correction methods.
Data Source
AI summary
An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.