Weight Sensor Array Addressing for Item Detection
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Solution Overview
Problem
Existing position tracking systems face challenges in scaling to larger spaces due to synchronization issues and computational limitations, which affect the accuracy and reliability of tracking people and objects in real-time applications.
Innovation Solution
A distributed tracking system utilizing an array of cameras, multiple camera clients, a camera server, weight sensors, and a central server, where camera clients process frames locally and communicate timestamps to synchronize coordinates, and weight sensors are assigned unique addresses for accurate item tracking, enabling edge computing and adaptive item counting algorithms to handle larger spaces effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a distributed tracking system with multiple camera clients and servers is implemented, then scalability to larger spaces is improved, but device complexity increases
Solution Approach 1:
The tracking system is divided into multiple independent camera clients, each handling a subset of cameras and processing frames locally. Each camera client operates autonomously to reduce coordination overhead, while weight sensors are segmented and assigned unique addresses for individual tracking. This segmentation enables the system to scale to larger spaces without proportionally increasing central processing complexity.
Solution Approach 2:
The system transitions from centralized frame processing to a distributed architecture where processing occurs across multiple dimensions - local processing at camera clients, regional aggregation at camera servers, and global coordination at central servers. This multi-dimensional processing hierarchy enables scalable deployment across large spaces while maintaining manageable complexity at each level.
2Measurement precision
If camera clients process frames locally, then synchronization accuracy is improved, but computational resource requirements increase
Solution Approach 1:
Computational workload is segmented across multiple camera clients, each processing frames from a subset of cameras locally. This distributes the computational burden while maintaining synchronization accuracy through local timestamp generation. The segmentation prevents any single device from requiring excessive computational power while collectively achieving high precision synchronization.
Solution Approach 2:
Camera servers act as intermediaries between camera clients and the central server, aggregating timestamped coordinates from multiple clients before forwarding to central processing. This intermediary layer reduces the computational burden on individual camera clients by handling coordination and aggregation tasks, while still enabling accurate synchronization through the distributed timestamp system.
3Measurement precision
If weight sensors are assigned unique addresses, then item tracking accuracy is improved, but device complexity increases
Solution Approach 1:
Weight sensors are configured with unique addresses that enable self-identification and self-tracking within the distributed system. Each sensor autonomously associates weight change events with its unique address, eliminating the need for complex external configuration or manual tracking setup. This self-service approach improves item tracking accuracy while minimizing configuration complexity through automated address assignment and event correlation.
Data Source
AI summary
An item position tracking system includes weight sensors each associated with a weight board. Each weight sensor transmits sensor data indicative of a weight of an item to its corresponding weight board. Each weight board is configured to assign a particular address number to its corresponding weight sensor. The weight boards transmit the sensor data and the address numbers to a circuit board that transmits the sensor data and the address numbers to a weight server. The weight server determines from which weight sensor data is originated based on the address numbers, and whether items were removed from the weight sensors.