Systems and methods for automated design of camera placement and cameras arrangements for autonomous checkout
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Solution Overview
Problem
Existing technologies face challenges in determining the optimal placement of cameras in real space to effectively track subjects and their interactions, such as puts and takes, in large spaces like shopping stores, while minimizing the number of cameras and costs.
Innovation Solution
A computer-implemented method that uses machine learning to iteratively optimize the number and pose of cameras based on a three-dimensional map of the space, applying constraints like physical obstructions and coverage thresholds to improve camera coverage without increasing the number of cameras.
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 operational cost increases
Solution Approach 1:
The system changes parameters such as camera pose, position, and orientation to optimize coverage. By adjusting these parameters iteratively through machine learning, the system achieves improved coverage scores without necessarily increasing the number of cameras, thus resolving the contradiction between coverage quality and quantity of cameras.
Solution Approach 2:
The camera placement plan is made dynamic and adaptive through iterative machine learning optimization. The system can adjust camera poses and positions based on learned patterns from simulated shopper interactions, allowing the same number of cameras to achieve better coverage through optimized dynamic placement rather than static increases in camera quantity.
2Quantity of substance
If the number of cameras is reduced to lower cost, then the quantity of cameras is reduced, but the coverage score deteriorates
Solution Approach 1:
By optimizing camera parameters (pose, position, orientation) through machine learning, the system enables fewer cameras to achieve the same or better coverage scores. The parameter optimization allows each camera to be positioned and oriented more effectively, compensating for the reduced number of cameras.
Solution Approach 2:
The system uses simulated environments to copy and test camera placements before actual deployment. Through simulation of shopper interactions and iterative testing, the system identifies optimal camera configurations that maximize coverage with minimal cameras, allowing virtual optimization to guide physical deployment.
3Reliability
If machine learning optimization is applied to improve coverage, then the coverage score is improved, but the computational complexity and processing time increases
Solution Approach 1:
The system performs preliminary optimization in a simulated environment before actual deployment. By pre-computing optimal camera placements through machine learning in simulation, the complex computational work is done beforehand, allowing the actual deployment system to use pre-optimized configurations without requiring real-time complex calculations.
Solution Approach 2:
A simulation environment acts as an intermediary between the camera placement problem and actual deployment. The simulation mediates the complex machine learning optimization process, allowing iterative testing and refinement of camera configurations without the computational burden affecting real-time operational systems.
Data Source
AI summary
Systems and techniques 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.