Fatigue Detection via Cloud-Edge Segmentation and Facial Region Extraction
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Solution Overview
Problem
Existing fatigue state detection systems in vehicles face challenges in accurately determining fuzzy state changes due to limited compute resources, often leading to missed or false fatigue state detections.
Innovation Solution
A method and apparatus that utilize a camera in a vehicle to obtain image blocks of a driver's organ area, sending these to a cloud server for further analysis when a preset fatigue state type is detected, leveraging the cloud server's more powerful resources for accurate fatigue level determination and reducing data transmission volume while protecting user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fatigue state detection is performed using only local mobile device resources, then device complexity is reduced, but measurement precision deteriorates due to limited compute resources
Solution Approach 1:
The system divides the fatigue detection process into two segments: initial fatigue state type judgment performed locally on the mobile device, and detailed fatigue level detection performed on the cloud server. This segmentation allows the mobile device to maintain simplicity while the cloud server provides enhanced computational precision for accurate fatigue detection.
Solution Approach 2:
The cloud server acts as an intermediary between the mobile device and the final fatigue detection result. The mobile device sends video frames to the cloud server, which then performs comprehensive analysis and returns fatigue level information, enabling high-precision detection without increasing local device complexity.
2Measurement precision
If complete video frames are transmitted to cloud server, then measurement precision improves, but loss of substance increases due to large data transmission volume
Solution Approach 1:
The system extracts only the necessary facial region from complete video frames before transmitting to the cloud server. By taking out and transmitting only the relevant facial area rather than entire video frames, the system maintains detection precision while significantly reducing data transmission volume and network resource consumption.
Solution Approach 2:
The system applies local quality processing by focusing computational and transmission resources on the facial region where fatigue indicators are located. Instead of transmitting and processing entire video frames uniformly, the system concentrates on the locally relevant facial area, optimizing both precision and efficiency.
3Measurement precision
If facial region data is transmitted to cloud server, then measurement precision improves, but object-generated harmful factors increase due to user privacy exposure
Solution Approach 1:
The system extracts and transmits only the minimum necessary facial region data required for fatigue detection to the cloud server. By taking out only the essential organ area information rather than complete facial images, the system achieves improved detection precision while minimizing privacy exposure and reducing the scope of sensitive data transmitted over the network.
Data Source
AI summary
Disclosed are a fatigue state detection method and apparatus, a medium and a device. The method includes: obtaining image blocks containing an organ area of a target object from a plurality of video frames collected by a camera apparatus disposed in a mobile device, to obtain an image-block sequence that is based on the organ area; determining a fatigue state type of the target object based on the image-block sequence of the organ area; sending the image-block sequence to a cloud server if the fatigue state type meets a first preset type, and rendering the cloud server to detect a fatigue level of the target object based on the image-block sequence; and receiving fatigue level information about the target object that is returned by the cloud server. The present disclosure may improve accuracy of fatigue state detection, thereby helping to improve driving safety of the mobile device.