AI Gateway Model Selection for Resource-Limited Video Classification
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Solution Overview
Problem
Unmanned systems with limited computation resources face challenges in using third-party machine learning models for object detection, as they require efficient resource management to operate effectively in operational scenarios.
Innovation Solution
An AI gateway system processes video streams by selecting and activating only the necessary machine learning models for object recognition, based on operator input, to conserve resources and optimize processing, allowing for the use of multiple models from different entities to identify specific object types within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are used for object detection, then object recognition capability is improved, but computation resource consumption increases
Solution Approach 1:
The system dynamically selects and activates machine learning models based on operational requirements and available resources. The AI gateway can enable or disable specific models (e.g., land-based weapon systems, aerial weapon systems, human beings) depending on the operational scenario, allowing the system to adapt its computation resource usage while maintaining necessary object recognition capabilities.
Solution Approach 2:
The patent divides the object detection task into multiple specialized machine learning models, each trained to identify specific object types. Instead of using a single comprehensive model that processes all object types, the system segments the detection functionality into separate models (e.g., one for land-based weapons, another for aerial weapons), allowing selective activation based on operational needs and reducing overall resource consumption.
2Adaptability or versatility
If third-party machine learning models are used, then object detection functionality is improved, but system complexity increases
Solution Approach 1:
The AI gateway serves as an intermediary layer between the operator/controller and multiple third-party machine learning models. It manages the complexity by providing a unified interface for model selection, configuration, and execution. The gateway handles model loading, parameter adjustment, and coordination, shielding the operator from the underlying complexity of managing multiple diverse models while enabling access to their enhanced detection capabilities.
3Ease of operation
If operator-selectable object types are implemented, then operational control is improved, but system complexity increases
Solution Approach 1:
The AI gateway provides a universal interface that handles multiple object types and model configurations through a single system. The controller presents a unified graphical user interface where operators can select from available object types regardless of which specific machine learning models are underlying. This multi-functional approach allows the system to support diverse detection needs while maintaining a consistent, simple interface for operators.
Data Source
AI summary
Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.