Adaptive Face Tracking Using Behavioral-State Inference Control
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Solution Overview
Problem
Existing face tracking technologies consume excessive computing resources and power due to the need for real-time computer vision analysis, leading to high resource costs.
Innovation Solution
A face tracking device and system that dynamically adjusts the inference rate of a neural network based on the behavioral state of the user, reducing resource consumption by varying the processing intensity according to user activity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision analysis is performed on a large number of real-time images, then face tracking accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system dynamically adjusts the inference rate of the neural network based on the behavioral state of the user. When the user is in an active state, the inference rate is increased to improve face tracking accuracy. When the user is in an inactive state, the inference rate is decreased to reduce computing resource consumption. This dynamic adjustment resolves the contradiction between accuracy and resource consumption by making the processing intensity adaptive to actual needs.
Solution Approach 2:
The system changes the inference rate parameter of the neural network based on user behavioral state detection. By modifying this key parameter dynamically, the system can optimize the balance between face tracking precision and computing resource usage, allowing high accuracy when needed and low consumption when not needed.
2Productivity
If real-time image analysis is performed continuously, then face tracking performance is improved, but power consumption increases
Solution Approach 1:
The system performs face tracking analysis periodically rather than continuously. By detecting user behavioral states and adjusting the inference rate accordingly, the system can maintain face tracking performance during active periods while reducing power consumption during inactive periods. This periodic action with variable intensity resolves the contradiction between continuous performance and power consumption.
Data Source
AI summary
A face tracking device, system, and method are provided. The device determines a behavioral state corresponding to a face area of a user based on multiple real-time images. The device adjusts the inference rate of a neural network based on the behavioral state. The device generates face tracking information corresponding to the user based on the real-time images and the neural network, and the neural network is controlled to execute based on the inference rate.