Homography Error Correction for Multi-Camera Object Tracking
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Solution Overview
Problem
Existing object detection and tracking systems face challenges in efficiently processing images from multiple cameras in large spaces, leading to computational intensity and incompatibility with real-time applications. Additionally, these systems struggle to determine the physical location of objects within images and to handle multiple object tracking effectively.
Innovation Solution
The system generates a homography that maps pixels from a sensor array to physical locations in a global coordinate system, enabling efficient object tracking across multiple sensors. This involves determining coefficients for the homography based on marker locations in the global plane and pixel locations in sensor images, allowing for accurate object positioning and handoff of tracking information between sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing techniques are used to identify objects in images from multiple cameras, then object identification capability is improved, but computational intensity increases and real-time processing becomes incompatible
Solution Approach 1:
The patent segments the large space into multiple zones, each monitored by a dedicated camera. Objects are tracked independently in each zone rather than processing all images globally, reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-establishing homography mappings between camera coordinates and global space coordinates during system setup. This allows real-time tracking to simply apply pre-computed transformations rather than calculating object locations from scratch in each frame.
2Measurement precision
If homography is used to map pixels to physical locations, then object positioning accuracy is improved, but system complexity increases due to coordinate transformation requirements
Solution Approach 1:
The patent introduces a global coordinate system as an intermediary between multiple camera coordinate systems. Homography transformations serve as the mathematical mediator that converts pixel coordinates from any camera into the unified global space, simplifying multi-camera integration.
Solution Approach 2:
The system changes the parameter representation of object locations from camera-specific pixel coordinates to global physical coordinates through homography transformation. This parameter change enables consistent positioning across multiple cameras while the transformation matrices are pre-computed to minimize real-time complexity.
3Area of stationary object
If multiple sensors are deployed to cover large spaces, then tracking coverage is improved, but information handoff between sensors becomes more complex
Solution Approach 1:
The patent creates a universal global coordinate system that all sensors reference. Tracking information from any sensor can be directly interpreted in global coordinates without requiring complex handoff protocols, as each sensor independently maps to the same global space through its own homography transformation.
Data Source
AI summary
An object tracking system includes a sensor and a controller. The sensor captures frames for a global plane for at least a portion of a space. The controller has a memory that stores a homography associated with the sensor and a processor. The processor receives a first frame from the sensor and identifies a pixel location within the first frame. The processor determines an estimated sensor location for the sensor by applying the homography to the pixel location, and determines a location difference between the estimated sensor location and an actual sensor location for the sensor. The processor then compares the location difference to a difference threshold level, and recomputes the homography in response to determining that the location difference exceeds the difference threshold level.