Camera Recalibration Using Static Structure Features
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Solution Overview
Problem
Existing camera calibration techniques in real-space tracking systems, such as cashier-less shopping systems, face challenges in accurately recalibrating cameras without disrupting operations, especially in dynamic environments like shopping stores where cameras can drift due to vibrations or intentional displacement, making it difficult to maintain accurate image processing.
Innovation Solution
A system and method for recalibrating cameras by processing images to extract feature descriptors, matching them with previous calibration images, calculating transformation information, and updating calibration as needed, using a trained neural network classifier to identify immobile structures like shelves, allowing for continuous calibration without clearing subjects or interrupting tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If common camera calibration techniques using special markers are used, then calibration accuracy is improved, but operational disruption increases due to need to clear the area and stop tracking
Solution Approach 1:
The system performs self-calibration by automatically detecting and using naturally occurring static structures (shelves, display units) in the environment as calibration references, eliminating the need for external markers and manual intervention. The calibration process runs autonomously in the background without requiring store closure or customer evacuation.
Solution Approach 2:
The system continuously identifies and tracks static structures in advance, building a database of reference points before calibration is needed. This preliminary mapping allows rapid recalibration when drift is detected, rather than requiring full calibration procedures at that moment.
2Measurement precision
If frequent recalibration is performed to maintain accuracy, then measurement precision is improved, but time loss increases due to repeated calibration interruptions
Solution Approach 1:
The calibration process continues uninterrupted during normal store operations. The system processes calibration data in real-time alongside tracking data, and performs recalibration in the background without stopping the primary function of monitoring customer behavior and item interactions.
Solution Approach 2:
Static environmental structures serve as intermediary reference objects that bridge the camera's coordinate system with the real-world coordinate system. These natural landmarks replace artificial markers and enable continuous calibration through their persistent presence in the field of view.
3Measurement precision
If manual calibration intervention is used, then calibration accuracy is improved, but device complexity increases due to need for manual setup and markers
Solution Approach 1:
The system replaces mechanical marker placement and manual adjustment procedures with automated computer vision algorithms. Neural networks and image processing automatically detect static structures, extract feature points, and compute calibration transformations without human physical intervention.
Solution Approach 2:
The same camera system used for tracking customers and items is also used for calibration purposes. The system simultaneously performs monitoring and self-calibration functions using the same hardware, eliminating the need for separate calibration equipment and reducing overall system complexity.
Data Source
AI summary
Automated techniques provide for recalibrating cameras configured for monitoring an area of real space. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. Transformation information between the selected images and the set of calibration images is obtained based on one or more features extracted from the selected images using a trained neural network classifier corresponding to points located at relatively immobile structures. The features extracted from the selected images match features in the set of calibration images. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.