Edge Device Triangulation for Passive Object Tracking
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Solution Overview
Problem
Existing systems for locating objects in operational areas face challenges due to passive objects that require external observations, which can be hindered by cost issues, environmental interference, or hardware/software failures, leading to unreliable location tracking.
Innovation Solution
A network of edge devices with image sensors and processors generates vectors to approximate the center point of objects, using pattern recognition criteria to determine the position of composite two-dimensional shapes across overlapping fields of view, enabling accurate location determination within operational areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive objects are tracked using external sensors (lasers, cameras, radar, sonar, induction sensors, infrared sensors), then location detection capability is improved, but system cost and vulnerability to environmental interference increase
Solution Approach 1:
The patent creates a virtual copy of the physical operational area using 2D shape representations and vector data from multiple edge devices. Instead of relying on expensive external sensors to directly detect passive objects, the system creates computational models (copies) of objects based on observations from distributed edge devices, thereby reducing dependency on costly specialized sensing hardware while maintaining location detection capability
Solution Approach 2:
The patent enables standard edge devices (cameras, image processors) to perform multiple functions: capturing images, generating 2D shapes, creating vectors, and participating in triangulation calculations. This multi-functionality eliminates the need for dedicated expensive sensors (lasers, radar, sonar) by making ordinary devices capable of location detection through software-based triangulation, thereby reducing system cost and environmental vulnerability
2Productivity
If onboard location capabilities are used in physical objects, then real-time location tracking is improved, but system reliability deteriorates due to cost issues, environmental interference, or hardware/software failures
Solution Approach 1:
The patent merges the location determination function from multiple independent edge devices into a unified triangulation system. Instead of relying on a single onboard location device that could fail, the system combines observations from multiple distributed edge devices, so that if one device fails or experiences interference, the system can still determine object location using data from other devices, thereby improving reliability while maintaining real-time tracking
Solution Approach 2:
The patent introduces a server as an intermediary that receives 2D shape and vector data from multiple edge devices, performs triangulation calculations, and determines object locations. This intermediary architecture isolates the critical location determination logic from individual edge devices and physical objects, so that failures in onboard hardware or software do not directly compromise the overall system reliability, as the server can compensate using data from functioning edge devices
Data Source
AI summary
An exemplary computing system for locating an object in an operational area is disclosed. The computing system having a server and plurality of edge devices. The edge devices having an image sensor configured to capture video data of the operational area from a specified location. The edge devices can process the video data to identify an object and generate a two-dimensional shape representative of the object, generate a vector from a lens of the image sensor through a center point of the two-dimensional shape; and determine relative position of the two-dimensional shape based on geospatial information of the edge device and the vector. The server and one or more of the edge devices receiving video data from a plurality of edge devices and generating a graphic, which defines a position of the object within the operational area based on the vector and location information of each edge device.