RFID Location Tracking via Computer Vision Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional RFID systems face inaccuracies in location tracking due to environmental interferences, as pre-defined signal propagation models are inflexible and unable to adapt to dynamic environments, leading to incorrect calculations of RFID tag locations.
Innovation Solution
Combining radio-frequency sensors with computer vision systems to create multiple location models tailored to various environment configurations, using cameras to provide accurate information on interfering objects and associate objects, allowing for selection of the most suitable model at runtime and continuous refinement of location models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-defined signal propagation models are used for RFID location tracking, then the system structure is simple, but the location accuracy deteriorates due to inability to adapt to dynamic environmental interferences
Solution Approach 1:
The patent implements dynamic environment configuration detection using computer vision systems that continuously monitor and adapt to changing environmental conditions. The system detects current environmental configurations and selects or adjusts location models in real-time, transforming the static pre-defined model approach into a dynamic adaptive system that maintains accuracy in changing conditions.
Solution Approach 2:
The system changes the parameters of location models based on detected environmental configurations. Different location models with varying parameters are selected according to the current environment, allowing the system to optimize location tracking accuracy for different conditions without requiring a completely complex system architecture.
2Measurement precision
If multiple location models tailored to various environment configurations are created, then the location accuracy improves, but the device complexity increases
Solution Approach 1:
The patent introduces computer vision systems as an intermediary that automatically detects environmental configurations and selects the appropriate location model. This intermediary component manages the complexity of having multiple location models by providing an automated selection mechanism, reducing the burden on users to manually manage model complexity while maintaining high accuracy.
Solution Approach 2:
The system implements self-service through automated environment detection and model selection. The computer vision system automatically monitors environmental conditions and selects the most appropriate location model without human intervention, allowing the system to maintain high location accuracy while managing complexity through automation rather than manual configuration.
3Ease of operation
If pre-defined signal propagation models are used, then the ease of operation is high, but the adaptability to dynamic environments deteriorates
Solution Approach 1:
The patent prepares multiple location models in advance for different environmental configurations, and the computer vision system automatically selects the appropriate pre-prepared model when environmental conditions change. This preliminary preparation of multiple models combined with automated selection maintains ease of operation while improving adaptability to dynamic environments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate location tracking and association of objects by accounting for environmental interferences, improving the precision of RFID signal interpretation and adapting to changing environments, ensuring reliable location determination and association relationships.
Implementation Method 1
Radio frequency identification (RFID) sensors can use electromagnetic fields to detect and identify RFID tags attached to objects
Implementation Method 2
The vision system can also provide the accurate location of the other object
Data Source
AI summary
The disclosure relates to tracking the location of a target object. In one example, a computer vision system detects a configuration of environment objects. A location model that has been trained for the environment configuration is selected. A signal associated with the target object is received and interpreted using the selected location model to determine the location of the target object.