Cascade Neural Network Attention Detection
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Solution Overview
Problem
Existing human attention detection methods face challenges in real-world environments due to varying illumination conditions, diverse human head postures and expressions, and differences in race, gender, and age, leading to high computational complexity and limited suitability for embedded devices.
Innovation Solution
An attention detection method utilizing a cascade neural network with a first convolutional neural network for preliminary inattention judgment and a second convolutional neural network for detailed inattention analysis, reducing overall computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a convolutional neural network model is used for attention detection, then detection accuracy and robustness are improved, but computational complexity increases and device suitability decreases
Solution Approach 1:
The patent divides the attention detection task into two stages using a cascade structure: a first convolutional neural network for preliminary detection and a second convolutional neural network for detailed analysis. This segmentation allows the system to use computational resources efficiently by applying complex processing only when necessary, thus reducing overall computational complexity while maintaining high detection accuracy.
Solution Approach 2:
The first convolutional neural network performs preliminary action by conducting initial attention detection before the second network processes the data. This preliminary screening filters out obvious cases, allowing the more computationally intensive second network to focus only on ambiguous or critical cases, thereby reducing total computational load while preserving accuracy.
2Measurement precision
If multiple complex operations such as face detection, face calibration, eyeball detection and feature extraction are performed, then detection comprehensive performance is improved, but system complexity and vulnerability to submodule failures increase
Solution Approach 1:
The patent merges multiple complex operations (face detection, face calibration, eyeball detection, feature extraction) into a unified convolutional neural network framework. The CNN automatically learns and integrates these operations through its layered architecture, eliminating the need for separate modular subcomponents and their associated complexity and failure risks.
Solution Approach 2:
The convolutional neural network serves multiple functions simultaneously: it performs face detection, extracts features, detects eyeball movement, and determines attention state. This multi-functionality consolidates what would otherwise require multiple specialized systems into a single versatile model, reducing overall system complexity.
3Ease of manufacture
If conventional machine learning methods and conventional feature extraction algorithms are used, then implementation simplicity is maintained, but generalization capability and adaptability to changing conditions deteriorate
Solution Approach 1:
The patent employs a convolutional neural network that automatically learns optimal parameters from training data, replacing hand-crafted conventional features. The network adapts its internal parameters during training to capture the essential characteristics of attention states, providing superior generalization capability while maintaining a relatively simple implementation structure.
Data Source
AI summary
The present invention provides an attention detection method based on a cascade neural network, a computer apparatus, and a computer-readable storage medium. The method includes: obtaining video data, recognizing a plurality of image frames, and extracting a face region of the plurality of image frames; recognizing the face region by using a first convolutional neural network to judge whether a first situation of inattention occurs; and recognizing, if it is confirmed that no first situation of inattention occurs, the face region by using a second convolutional neural network to judge whether a second situation of inattention occurs, where computational complexity of the first convolutional neural network is less than computational complexity of the second convolutional neural network. The present invention further provides the computer apparatus for implementing the foregoing method and the computer-readable storage medium.