Managing constraints for automated design of camera placement and cameras arrangements for autonomous checkout
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Solution Overview
Problem
Existing technologies face challenges in effectively and automatically determining the placement of cameras in large spaces to track shoppers and their interactions with inventory items, including puts, takes, and transfers, while minimizing costs.
Innovation Solution
A computer-implemented method that uses a machine learning process to iteratively apply to an objective function of the number and poses of cameras, subject to a set of constraints, to determine an improved camera coverage plan. This plan includes the number, placement, and pose of cameras to track puts and takes of items by subjects in a three-dimensional real space, potentially using fewer cameras while maintaining or improving coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of cameras is increased to improve coverage of the area, then the coverage score is improved, but the installation and operation cost increases
Solution Approach 1:
The system changes the parameters of camera placement (position, orientation, height) and uses machine learning optimization to find the optimal configuration that maximizes coverage while minimizing the number of cameras required. The objective function optimizes coverage score subject to constraints on minimum coverage thresholds and camera quantity.
2Adaptability or versatility
If manual methods are used to determine camera placement, then flexibility in adjustment is improved, but the time and complexity of the planning process increases
Solution Approach 1:
The system performs automated camera placement optimization without requiring manual intervention. The machine learning model automatically processes the three-dimensional map, applies constraints, and generates the optimal camera coverage plan, eliminating the need for time-consuming manual planning while maintaining adaptability through configurable parameters.
3Reliability
If cameras are placed to ensure complete coverage of all areas, then detection reliability is improved, but the number of cameras required increases
Solution Approach 1:
The system applies partial coverage by setting minimum coverage thresholds for different areas (e.g., shelves, aisles, checkout areas) rather than requiring uniform complete coverage everywhere. The objective function ensures that critical areas meet their specific coverage requirements while allowing less critical areas to have reduced coverage, thereby reducing the total number of cameras needed.
Data Source
AI summary
Techniques for managing coverage constraints are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.