Role-Based Machine Learning Models for Context-Aware Video Analysis
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Solution Overview
Problem
Existing video cameras equipped with a single machine learning model for object recognition face limitations in processing resources and inefficiencies due to the need to process all potential objects regardless of the scene context, such as police or fire incidents.
Innovation Solution
Implementing a supervisory machine learning model to determine specific roles within a scene, such as 'police' or 'firefighter', and selectively activate relevant role-specific machine learning models for efficient analysis, reducing unnecessary processing by switching between models based on detected roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for object recognition in video cameras, then the device complexity is reduced, but the analysis precision and processing efficiency deteriorate due to inability to distinguish between different incident types
Solution Approach 1:
The patent divides the machine learning model into multiple role-specific models (e.g., police incident model, fire incident model, medical incident model). Each model is trained to recognize objects and patterns specific to its incident type. A supervisory model first determines the incident type, then activates the appropriate specialized model for analysis, replacing the single monolithic model with segmented specialized models.
Solution Approach 2:
The system dynamically switches between different machine learning models based on the detected incident type. The supervisory model continuously monitors the scene and activates different role-specific models as needed, making the system adaptive and flexible rather than static. This dynamic model selection allows the system to optimize performance for each specific incident type while maintaining overall system simplicity.
2Measurement precision
If all potential objects are processed regardless of scene context, then the measurement precision for specific object detection is improved, but the use of processing resources deteriorates due to excessive computation on irrelevant objects
Solution Approach 1:
The supervisory model performs preliminary detection to identify the incident type and activate only the necessary role-specific models before processing objects in the scene. This preliminary classification action prevents unnecessary processing of objects that would be irrelevant to the current incident type, optimizing resource usage while maintaining detection precision for relevant objects.
Solution Approach 2:
Instead of applying uniform processing to all objects in the scene, the system applies different processing quality and models to objects based on their relevance to the detected incident type. Objects relevant to the active role-specific model receive detailed processing, while objects irrelevant to the current incident type are either ignored or processed with minimal resources, creating localized quality variation.
3Productivity
If role-based machine learning models are implemented, then the processing efficiency and resource optimization are improved, but the device complexity increases due to multiple models and supervisory architecture
Solution Approach 1:
The supervisory model serves multiple functions: it detects objects in the scene, classifies incident types, determines which role-specific models to activate, and coordinates the overall processing workflow. This multi-functional design consolidates control logic into a single component, reducing the need for separate complex coordination mechanisms and managing system complexity while maintaining high processing efficiency.
Data Source
AI summary
A device, system, and method for implementing role-based machine learning models is provided. A implements a supervisory machine learning model that detects a first role associated with a first object in images received from a camera. The device, from machine learning models, selects and implements a first machine learning model associated with the first role to perform analysis on the images, that is reported to one or more communication devices. After implementing the first machine learning model, the device uses the supervisory machine learning model to determine a second role associated with a second object in the images that is different from the first role, and selects, from the machine learning models, a second machine learning model associated with the second role. The device implements the second machine learning model to perform analysis on the images to change the analysis reported to the one or more communication device.