Camera Calibration Using Sequential Optical Targets
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Solution Overview
Problem
Current systems for monitoring inventory movement and user tracking in materials handling facilities lack efficient calibration methods for cameras, leading to potential errors and increased system complexity.
Innovation Solution
A calibration process using target assemblies with optical targets that transition between detectable states, allowing cameras to associate pixel coordinates with real coordinates in 3D space, enabling accurate tracking of inventory and users without synchronization between targets and cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional camera calibration methods are used, then calibration accuracy may be maintained, but system complexity increases and calibration efficiency decreases
Solution Approach 1:
The calibration system is segmented into multiple independent target assemblies distributed throughout the facility, each with its own optical targets. This allows calibration to be performed independently at multiple locations simultaneously, reducing overall system complexity while maintaining accuracy through distributed measurement points
Solution Approach 2:
Optical targets serve as intermediaries between the cameras and the physical objects being tracked. These targets provide known reference points that cameras can detect and use to establish coordinate transformations, simplifying the calibration process while ensuring measurement precision through the well-defined optical properties of the targets
2Measurement precision
If synchronization between targets and cameras is implemented, then calibration accuracy improves, but device complexity and operational difficulty increase
Solution Approach 1:
Optical targets transition between detectable states in a periodic or sequential manner, allowing cameras to capture images at different times and still achieve accurate calibration. This eliminates the need for simultaneous synchronization between all targets and cameras, as each target's state transitions provide sufficient reference information when captured at different moments
Solution Approach 2:
The calibration process uses preliminary captured images of targets in different states to establish coordinate relationships before actual tracking begins. By pre-capturing target positions and states, the system establishes calibration data without requiring real-time synchronization during operation, reducing operational complexity
3Area of stationary object
If multiple cameras are deployed for comprehensive monitoring, then coverage area increases, but calibration time and computational requirements increase
Solution Approach 1:
The facility is divided into multiple zones, each monitored by specific cameras and calibrated using nearby target assemblies. This segmentation allows parallel calibration of multiple camera zones simultaneously, increasing overall coverage area while reducing total calibration time through concurrent processing of multiple calibration datasets
Solution Approach 2:
Target assemblies serve multiple functions: they provide calibration references for multiple cameras, establish coordinate transformations across different viewing angles, and enable verification of calibration accuracy. This multi-functionality reduces the need for separate calibration procedures for each camera, decreasing overall calibration time while maintaining comprehensive coverage
Data Source
AI summary
Cameras may be used to acquire information about objects in three-dimensional (3D) space. Described herein are techniques for determining a calibration between pixel coordinates in a two-dimensional image acquired by the camera and coordinates in the 3D space. In one implementation, the target assembly is positioned at a known 3D location and particular optical targets thereon having known 3D coordinates are activated sequentially. Images are acquired and processed to determine the pixel coordinates of the respective optical targets. Calibration data, such as a transformation matrix, is generated based on the information about which optical target is active in a given acquired image, the pixel coordinates of the optical target in that acquired image, and the known 3D coordinates of the individual optical target.