Design of cloud self-studying intelligent detection and supervision system based on Dlib model
The development of cloud self-study is of great significance for cultivating students'self-learning ability and promoting the construction of smart education.However,user supervision for cloud self-study is facing difficulties.The existing video surveillance methods still require the use of complete process video recordings to observe the user's self-study status.Moreover,there is a significant risk of privacy leakage when complete video surveillance data is uploaded to the cloud server.To address the aforementioned issues,this paper proposes a Dlib model based facial keypoint detection algorithm that analyzes the changes in human behavior characteristics during user self-study through video stream analysis,achieving intelligent recognition and detection of user self-study status.Based on the above detection algorithms,this article implements a cloud self-learning intelligent detection and supervision system.The system does not need to store complete video data for review,and can fully display the changes in the user's self-study status and self-study results,fully protecting the user's privacy.The experimental data under the simulation of self-study scenarios shows that the detection accuracy of the system reaches an average of over 80%,and it can process video streams of more than 20 frames per second in real-time,meeting the requirements of accuracy and real-time detection of self-study status.
cloud self-studyingbehavioral testingintelligent supervisionimage processingDlib model
NETL
NSTL
万方数据
