Dynamic Recognition Resource Deployment in Distributed Camera Systems
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Solution Overview
Problem
Existing camera devices are often installed with sub-optimal executable recognition resources, which may not be suitable for specific video recognition applications or changing available capacities, leading to inadequate performance in dynamic environments.
Innovation Solution
A method for dynamic deployment of optimized executable recognition resources in distributed camera systems, where a processor circuit identifies the deployment context, determines the best matching resource, and sends instructions for deployment and execution, enabling adaptive use of different resources and sharing strategies based on video recognition requirements and available capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a camera device is installed with a particular executable recognition resource during manufacture or deployment, then the device has a fixed recognition capability, but the recognition performance becomes sub-optimal or inadequate for changing video recognition applications
Solution Approach 1:
The patent implements dynamic deployment of executable recognition resources, allowing the camera device to transition from a static resource configuration to a dynamic one where resources can be selected and deployed based on current video recognition requirements and available device capacity, thereby improving adaptability without requiring complete system redesign
Solution Approach 2:
The system enables self-service through automated identification of deployment context, automatic determination of optimized resources, and autonomous deployment instructions, eliminating the need for manual intervention and reducing the operational complexity of managing multiple recognition resources
2Measurement precision
If the camera device uses a fixed executable recognition resource, then the device structure remains simple, but the recognition accuracy and performance become inadequate for specific video recognition applications
Solution Approach 1:
The patent changes the operational parameters of the camera device by introducing dynamic selection among multiple executable recognition resources with different characteristics (e.g., SIFT for accuracy, SURF for speed), allowing the system to optimize recognition accuracy for specific applications by selecting the appropriate resource based on deployment context
3Productivity
If the camera device deploys optimized executable recognition resources dynamically, then the recognition performance is optimized for specific applications, but the system complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary action by pre-identifying and evaluating multiple executable recognition resources with their respective characteristics before deployment, and by establishing a framework for automatic context identification and resource matching, thereby reducing the computational overhead during actual recognition operations
Data Source
AI summary
In one embodiment, a method comprises: identifying a deployment context for execution within one or more distributed camera devices in a distributed camera system, the deployment context including video recognition requirements relative to available capacity in the one or more distributed camera devices; determining an optimized executable recognition resource for the deployment context from available executable recognition resources; and sending, to the one or more distributed camera devices, an instruction for deployment and execution of the optimized executable recognition resource for optimized recognition according to the deployment context.