Centralized AI Data Stream Processing for Multi-Source Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic systems equipped with multiple recording devices face challenges in efficiently processing diverse data types (e.g., video, infrared, audio, heat) due to the need for different machine learning models, which often require significant processing resources and are not efficiently managed by computing devices in the field.
Innovation Solution
A data stream processing system at a central location receives and processes multiple data streams from unmanned vehicles, determines the appropriate machine learning model for each type of data, identifies objects within the streams, and generates composite frames by synchronizing and overlaying object indicators across different data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple dedicated machine learning models are used to process different data types from multiple unmanned vehicles, then object detection accuracy is improved, but processing complexity and resource requirements increase significantly
Solution Approach 1:
The patent combines multiple dedicated machine learning models into a single centralized processing system that handles multiple data types (video, infrared, audio, thermal) from multiple unmanned vehicles. This consolidation maintains the specialized processing capabilities of individual models while reducing overall system complexity and resource requirements through shared infrastructure.
Solution Approach 2:
The centralized processing system is designed to universally handle multiple types of data streams from various sources simultaneously. It can process video frames, infrared images, audio signals, and thermal data using appropriate machine learning models, making the system multi-functional rather than requiring separate dedicated systems for each data type.
2Speed
If computing devices in the field process data from multiple unmanned vehicles with different data types, then real-time processing capability is improved, but processing efficiency decreases due to resource constraints
Solution Approach 1:
The patent extracts the heavy processing workload from field computing devices and relocates it to a centralized processing system. This allows field devices to maintain real-time responsiveness while the centralized system handles the computationally intensive tasks of processing multiple data types from multiple vehicles, thereby improving overall processing efficiency.
3Measurement precision
If different machine learning models are deployed for different data types, then data processing accuracy is improved, but system resource requirements increase
Solution Approach 1:
The patent merges multiple specialized machine learning models into a single centralized processing platform, allowing the system to maintain high processing accuracy for each data type while sharing computational resources. This reduces redundant resource consumption that would occur if separate dedicated systems processed each data type independently.
Data Source
AI summary
Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.